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Campbell Systematic Reviews 2015:19 First published: 1 November 2015 Search executed: 2013
Economic Self-Help Group Programs for Improving Women’s Empowerment: A Systematic Review
Carinne Brody, Thomas de Hoop, Martina Vojtkova, Ruby Warnock, Megan Dunbar, Padmini Murthy, Shari L. Dworkin
Colophon
Title Economic Self-Help group Programs for Improving Women’s
Empowerment: A Systematic Review
Institution The Campbell Collaboration
Authors Brody, Carinne
De Hoop, Thomas
Vojtkova, Martina
Warnock, Ruby
Dunbar, Megan
Murthy, Padmini
Dworkin, Shari L.
DOI 10.4073/csr.2015.19
No. of pages 182
Citation Brody C, De Hoop T, Vojtkova M, Warnock R, Dunbar M, Murthy P,
Dworkin S. Economic Self-Help group Programs for Improving Women’s
Empowerment: A Systematic Review.
Campbell Systematic Reviews 2015:19
DOI: 10.4073/csr.2015.19
ISSN 1891-1803
Copyright © Brody et al.
This is an open-access article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted use, distribution,
and reproduction in any medium, provided the original author and source
are credited..
Roles and
responsibilities
The study was led by Carinne Brody (CB). This report was written by CB and
Thomas de Hoop (TH). CB, Megan Dunbar (MD), Padmini Murthy (PM) and
Shari Dworkin (SD) authored the study protocol. The search strategy was
developed by CB, MD and Tara Horvath, a search specialist from the
University of California, San Francisco. CB and Ruby Warnock (RW), along
with several research assistants, conducted the search. The meta-analysis
was undertaken by TH and Martina Vojtkova. The qualitative synthesis was
undertaken by CB and RW. RW edited the report. CB and TH will be
responsible for updating this review as additional evidence accumulates and
as funding becomes available.
Editors for
this review
Editor: Hugh Waddington
Managing Editor: Emma Gallagher
Sources of support International Initiative for Impact Evaluation (3ie)
Declarations of
interest
The authors declare that they are not aware of any conflicts of interests.
Corresponding
author
Carinne Brody
Touro University
1301 Club Drive
Mare Island
Vallejo, California 94592
USA
E-mail: [email protected]
Campbell Systematic Reviews
Editor-in-Chief Julia Littell, Bryn Mawr College, USA
Editors
Crime and Justice David B. Wilson, George Mason University, USA
Education Sandra Wilson, Vanderbilt University, USA
International
Development
Birte Snilstveit, 3ie, UK
Hugh Waddington, 3ie, UK
Social Welfare Nick Huband, Institute of Mental Health, University of Nottingham, UK
Geraldine Macdonald, Queen’s University, UK & Cochrane Developmental,
Psychosocial and Learning Problems Group
Methods Therese Pigott, Loyola University, USA
Emily Tanner-Smith, Vanderbilt University, USA
Chief Executive
Officer
Howard White, The Campbell Collaboration
Managing Editor Karianne Thune Hammerstrøm, The Campbell Collaboration
Co-Chairs
Crime and Justice David B. Wilson, George Mason University, USA
Peter Neyroud, University of Cambridge, UK
Education Sarah Miller, Queen's University Belfast, UK
Gary W. Ritter, University of Arkansas, USA
Social Welfare Brandy Maynard, Saint Louis University, USA
Mairead Furlong, National University of Ireland, Maynooth, Ireland
International
Development
Peter Tugwell, University of Ottawa, Canada
Hugh Waddington, 3ie, India
Methods Ian Shemilt, University of Cambridge, UK
Ariel Aloe, University of Iowa, USA
The Campbell Collaboration (C2) was founded on the principle that
systematic reviews on the effects of interventions will inform and help
improve policy and services. C2 offers editorial and methodological support
to review authors throughout the process of producing a systematic review. A
number of C2's editors, librarians, methodologists and external peer-
reviewers contribute.
The Campbell Collaboration
P.O. Box 7004 St. Olavs plass
0130 Oslo, Norway
www.campbellcollaboration.org
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Table of contents
PLAIN LANGUAGE SUMMARY 4
EXECUTIVE SUMMARY 5
Background 5
Objectives 5
Search methods 5
Selection criteria 5
Data collection and analysis 6
Results 6
Implications for policy, practice and research 7
1 BACKGROUND 9
1.1 Description of the problem 9
1.2 Description of the intervention 9
1.3 How the intervention might work 11
1.4 Why it is important to do this review 13
2 OBJECTIVES OF THE REVIEW 17
3 METHODS 18
3.1 Criteria for including studies in the review 18
3.2 Search methods for identification of studies 22
3.3 Data collection and analysis 25
3.4 Deviations from the protocol 37
4 RESULTS 39
4.1 Results of the search 39
4.2 Description of included studies 42
4.3 Critical appraisal of included studies 54
4.4 Synthesis of quantitative studies 58
4.5 Synthesis of qualitative studies 84
4.6 Integrated synthesis 97
5 DISCUSSION 102
5.1 Summary of main results 102
5.2 Overall completeness and applicability of evidence 103
5.3 Quality of the evidence 104
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5.4 Limitations and potential biases in the review process 105
5.5 Agreements and disagreements with other studies or reviews 108
6 AUTHORS’ CONCLUSIONS 109
6.1 Implications for practice and policy 109
6.2 Implications for research 110
7 ACKNOWLEDGMENTS 112
8 REFERENCES 113
Included Quantitative Studies (review objective 1) 113
Included Qualitative Studies (review objective 2) 115
Excluded Studies 116
9 APPENDICES 132
Appendix 1: Data extraction form 132
Appendix 2: Full search strategy 134
Appendix 3: Search diary 141
Appendix 4: Reasons for exclusion of marginal studies 144
Appendix 5: Qualitative study quality assessment 147
Appendix 6: Quantitative risk of bias assessment tool 150
Appendix 7: Overview of risk of bias assessment of included quantitative studies 155
Appendix 8: Detailed risk of bias assessment of included quantitative studies 156
Appendix 9: Quality assessment for included qualitative studies 164
Appendix 10: Procedures for calculating effect sizes 166
Appendix 11: Additional forest plots 169
Appendix 12: Additional quotes by theme 175
10 CONTRIBUTION OF AUTHORS 180
11 DECLARATIONS OF INTEREST 181
12 SOURCES OF SUPPORT 182
External sources 182
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Plain language summary
Motivation: Self-help groups (SHGs) are implemented around the world to
empower women, supported by many developing country governments and
agencies. A relatively large number of studies purport to demonstrate the
effectiveness of SHGs. This is the first systematic review of that evidence.
Approach: We conducted a systematic review of the effectiveness of women’s
economic SHG programs, incorporating evidence from quantitative and qualitative
studies. We systematically searched for published and unpublished literature, and
applied inclusion criteria based on the study protocol. We critically appraised all
included studies and used a combination of statistical meta-analysis and meta-
ethnography to synthesize the findings based on a theory of change.
Findings from quantitative synthesis: Our review suggests that economic SHGs
have positive effects on various dimensions of women’s empowerment, including
economic, social, and political empowerment. However, we did not find evidence for
positive effects of SHGs on psychological empowerment. Our findings further
suggest there are important variations in the impacts of SHGs on empowerment that
are associated with program design and contextual characteristics.
Findings from qualitative synthesis: Women’s perspectives on factors determining
their participation in, and benefits from, SHGs suggest various pathways through
which SHGs could achieve the identified positive impacts. Evidence suggested that
the positive effects of SHGs on economic, social, and political empowerment run
through the channels of familiarity with handling money and independence in
financial decision making, solidarity, improved social networks, and respect from
the household and other community members. In contrast to the quantitative
evidence, the qualitative synthesis suggests that women participating in SHGs
perceive themselves to be psychologically empowered. Women also perceive low
participation of the poorest of the poor in SHGs due to various barriers, which could
potentially limit the benefits the poorest could gain from SHG membership.
Findings from integrated synthesis: Our integration of the quantitative and
qualitative evidence suggests there is no evidence for adverse effects of women’s
SHGs on the likelihood of domestic violence. Women’s perspectives in the
qualitative research indicate that even if domestic violence occurs in the short term,
in the long term the benefits from SHG membership may mitigate the initial adverse
consequences of SHGs on domestic violence.
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Executive summary
BACKGROUND
Women bear an unequal share of the burden of poverty globally due to societal and
structural barriers. One way that governments, development agencies, and
grassroots women’s groups have tried to address these inequalities is through
women’s SHGs. This review focuses on the impacts of SHGs with a broad range of
collective finance, enterprise, and livelihood components on women’s political,
economic, social, and psychological empowerment.
OBJECTIVES
The primary objective of this review was to examine the impact of women’s
economic SHGs on women’s individual-level empowerment in low- and middle-
income countries using evidence from rigorous quantitative evaluations. The
secondary objective was to examine the perspectives of female participants on their
experiences of empowerment as a result of participation in economic SHGs in low-
and middle-income countries using evidence from high-quality qualitative
evaluations. We conducted an integrated mixed-methods systematic review that
examined data generated through both quantitative and qualitative research
methods.
SEARCH METHODS
We searched electronic databases, grey literature, relevant journals and organization
websites and performed keyword hand searches and requested recommendation
from key personnel. The search was conducted from March 2013–February 2014.
SELECTION CRITERIA
We included studies conducted from 1980–January 2014 that examined the impact
of SHGs on the empowerment of and perspectives of women of all ages in low- and
middle-income countries, as defined by the World Bank, who participated in SHGs
in which female participants physically came together and received a collective
finance and enterprise and/or livelihoods group intervention. To be included in the
review, quantitative studies had to measure economic empowerment, political
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empowerment, psychological empowerment or social empowerment. We also
examined adverse outcomes including intimate partner violence, stigma,
disappointment, and reduced subjective well-being. We included quantitative
studies with experimental designs using random assignment to the intervention and
quasi-experimental designs with non-random assignment (such as regression
discontinuity designs, “natural experiments,” and studies in which participants self-
select into the program). In addition, we included qualitative studies that explored
empowerment from the perspectives of women participants in SHGs using in-depth
interviews, ethnography/participant observation, and focus groups.
DATA COLLECTION AND ANALYSIS
We systematically coded information from the included studies and critically
appraised them. We conducted statistical meta-analysis from the data extracted
from quantitative experimental and quasi-experimental studies, and used meta-
ethnographic methods to synthesize the textual data extracted from the women’s
quotes in the qualitative studies. We then integrated the findings from the
qualitative synthesis with those from the quantitative studies to develop a
framework for assessing how economic SHGs might impact women’s empowerment.
RESULTS
We included a total of 23 quantitative and 11 qualitative studies in the final analysis.
Initially, we reviewed 3,536 abstracts from electronic database searches and 351
abstracts from the gray literature searches. We found that women’s economic SHGs
have positive statistically significant effects on various dimensions of women’s
empowerment, including economic, social and political empowerment ranging from
0.06-0.41 SD. We did not find evidence for statistically significant effects of SHGs on
psychological empowerment. We also did not find statistical evidence of adverse
effects of women’s SHGs. Our integration of the quantitative and qualitative
evidence indicates that SHGs do not have adverse consequences for domestic
violence. Our synthesis of women’s perspectives on factors determining their
participation in, and benefits from SHGs suggests various pathways through which
SHGs could achieve the identified positive impacts on empowerment. Women’s
experiences suggested that the positive effects of SHGs on economic, social, and
political empowerment run through several channels including: familiarity with
handling money and independence in financial decision making; solidarity;
improved social networks; and respect from the household and other community
members. Our synthesis of the qualitative evidence (key informant interviews and
focus groups) also indicates that women perceive there to be low participation of the
poorest of the poor in SHGs, as compared to less poor women.
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IMPLICATIONS FOR POLICY, PRACTICE AND RESEARCH
For Policy: SHGs can have positive effects on women’s economic, social, and
political empowerment. However, we did not find evidence for positive effects on
psychological empowerment. These findings indicate that donors can consider
funding women’s SHGs in order to stimulate women’s economic, social, and political
empowerment, but the effects of SHGs on psychological empowerment are less
clear. Women SHG members perceive that the poorest of the poor participate less
than other women. In part, this might be because the poorest of the poor are too
financially and/or socially constrained to join SHGs or to benefit from the financial
services most often provided through SHGs. Other barriers such as class or caste
discrimination might also be present. Poorer or marginalized women may not feel
accepted by groups that are made up of wealthier or more well-connected
community members. It is important for policy makers to identify ways to build in
support and reduce barriers for individual women who want to participate in SHGs
but who do not have the financial resources or freedoms to join.
For Practice: We do not find evidence for adverse effects of women SHGs on
domestic violence based on the integration of the quantitative and the qualitative
evidence. Although there may be adverse consequences in the short term, analysis of
women’s reports suggest that SHGs do not contribute to increases in domestic
violence in the long term. Furthermore, participation of the poorest of the poor in
SHGs may be stimulated by incentives. These incentives could be financial, for
example, by giving the poorest of the poor the opportunity to participate without a
savings requirements, or non-financial, for example, by stimulating the husbands or
mothers-in-law of the poorest of the poor to let their spouses and daughters-in-law
participate in SHGs or conducting outreach activities to marginalized groups. As
new programs are implemented in different contexts, it is also important that
program designs are tailored to the local settings in ways that allow them to evolve
over time. This review has shown that one-size does not fit all, and while it is
important to take best practices across programs for implementation, this means
that flexibility is required to adapt programs successfully for the greatest impact in
women’s lives.
For Research: There is a need for more rigorous quantitative studies that can
correct for selection bias, spillovers and the difficulties of measuring empowerment.
There is also a need for more research, focused on examining possible factors that
meditate and/or moderate the impact of SHGs on women’s empowerment to further
understand the pathways or mechanisms through which SHGs impact
empowerment. For the latter it is crucial to conduct rigorous qualitative research in
addition to rigorous quantitative research. Whereas quantitative research is useful in
understanding certain aspects of the impact of SHGs on empowerment, qualitative
studies could show us more nuanced ideas about how to measure empowerment.
Importantly, both quantitative and qualitative studies need to describe more fully
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the various components of the SHGs being studied. Greater detail in the description
of the program design will help in determining moderating factors in the design of
SHGs.
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1 Background
1.1 DESCRIPTION OF THE PROBLEM
Women bear an unequal share of the burden of poverty globally due to societal and
structural barriers. According to economist and Nobel laureate Amartya Sen (2001),
women worldwide have less access to “substantive freedoms” such as education,
employment, health care, and democratic freedoms. First, girls are enrolled in
school at lower rates than boys, resulting in women making up more than two-thirds
of the world’s illiterate adults (UNESCO, 2013). Second, women experience unequal
access to health care starting from birth and throughout their reproductive years
(WHO, 2007). Third, women are missing from all levels of government—local,
regional, and national (Lopez-Claros, 2005). Women also have fewer economic
freedoms. In sub-Saharan Africa, only 16 to 18 per cent of loans issued to small and
medium businesses are issued to women owners; and in South Asia, only 6 per cent
(IFC, 2014). In addition, in many countries, women cannot own land. In South and
Southeast Asia, women comprise more than 60 per cent of the agricultural labor
force. However, in India, Nepal, and Thailand less than 10 per cent of women
farmers own land (FAO, 2008). These facts describe what economists call the
feminization of poverty. This phrase is meant to capture women’s unequal share of
poverty, in terms of both wealth and choices and opportunities (Sen, 2001).
One way that governments, development agencies, and grassroots women’s groups
have tried to address these inequalities is through women’s economic self-help
groups (SHGs). The basic assumptions undergirding these income-generating group
programs are that giving women access to working capital can increase their ability
to “generate choices and exercise bargaining power as well as develop a sense of self-
worth, a belief in one’s ability to secure desired changes, and the right to control
one’s life” (UN, 2000). SHGs of women could facilitate these goals through the
development of social capital and mobilization of women (IFAD, 2003).
1.2 DESCRIPTION OF THE INTERVENTION
SHGs, also known as mutual aid or support groups, are small voluntary groups that
are formed by people related by an affinity for a specific purpose who provide
support for each other. They are created with the underlying assumption that when
individuals join together to take action toward overcoming obstacles and attaining
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social change, the result can be individual, and/or collective empowerment. SHG
members typically use strategies such as savings, credit, or social involvement as
instruments of empowerment. The types of SHGs that exist in developing countries
are numerous and can include economic, legal, health, and cultural objectives.
The canonical economic SHG model starts with an initial period of collective savings
in the name of the group to facilitate intragroup lending. The basic idea underlying
this model is that groups then gradually take larger loans, for example, from banks.
In addition, SHGs often provide support in the form of training, which can take
multiple forms. Trainings can, for example, focus on entrepreneurial skills, women’s
rights, political participation, basic education, and justice (Van Kempen, 2009).
SHGs can be linked directly with banks or can function through non-governmental
organizations (NGOs) and tend to be more fundamentally grassroots in nature than
the many microfinance institutes (MFIs) that now exist worldwide. Although SHGs
share some important characteristics, there are major differences across SHGs as
well. For example, Thorp et al. (2005) suggest that some SHGs focus on resolving
market failures, such as saving and credit constraints, while others put a stronger
emphasis on rights, for example group members’ rights to access resources or
political participation.
India and other countries in South and Southeast Asia have a long history of SHG
activity. South Asia’s largest and perhaps most well-known program is the Self-Help
Group-Bank Linkage Program (SBLP). This Indian program was started in 1992 and
has rapidly expanded since then. In 2009, the SBLP covered approximately 86
million poor households in 6.1 million saving-linked SHGs and 4.2 million credit-
linked SHGs. The SBLP is best known for its expansive outreach and high
repayment rates of over 95 per cent. The literature suggests that the program has
been effective at targeting poor women and is associated with improvements in
household income, livestock ownership, savings and households’ ability to withstand
economic shocks (Sinha, 2008). In addition, the program might have contributed to
improvements in women’s decision-making power, control over household
resources, and participation in the public sphere (Sinha, 2008). In other parts of the
world, such as sub-Saharan Africa and Latin America, the South Asian model has
been adapted to match the cultural and social context in those specific settings. For
example, SHGs in sub-Saharan Africa, such as Jeunes sans Frontières in Burkina
Faso, have a stronger emphasis on HIV/AIDS than SHGs in Asia. African SHGs may
thus have contributed to overcoming the stigma surrounding HIV/AIDS in sub-
Saharan Africa (Nguyen, 2005).
The majority of SHGs target women with the explicit goal of empowering them. For
example, the SHG model “was introduced as a core strategy to achieve
empowerment in the Ninth Plan (1997-2002) with the objective to ‘organize women
into Self-help group [sic] and thus mark the beginning of a major process of
empowering women’ (Planning Commission, 1997). Jakimow and Kilby (2006)
argue, however, that in practice the South Asian SHG model is often focused on
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solving market failures, by emphasizing credit and saving, rather than empowering
women.
This review focuses on SHGs that offer women a collective finance, enterprise,
and/or livelihood component. Collective finance and enterprise can include savings
and loans, group credit, collective income-generation, and micro-insurance.
Livelihood interventions can include life skills training, business training, financial
education, and labor and trade group organizing.
1.3 HOW THE INTERVENTION MIGHT WORK
Many different perspectives, definitions, measures, and outcomes have been
associated with women’s empowerment. The growing literature presents different
definitions of empowerment, and no one definition seems to be universally accepted.
For example, women’s empowerment is used interchangeably with other terms such
as women’s autonomy, status, and agency. These terms have subsequently been
measured in different ways. For example, women’s autonomy has been measured by
assessing the degree to which women participate in decision-making in their
households (Upadhyay, 2005) or by determining women’s mobility (Malhotra,
2002). Additional challenges in defining and measuring women’s empowerment
include variations in the cultural context that affect how empowerment may occur.
For example, women’s mobility may be a central issue to women’s empowerment in
one setting and a peripheral issue in another. Differences in the approach to
measure empowerment and contextual differences complicate the process of
defining whether different measures of empowerment can be considered part of the
same construct in this systematic review. We will discuss this issue in detail in later
stages of this review.
Nonetheless, much of the research agrees that empowerment is a process and an
outcome that can occur at multiple levels and within different dimensions. After the
International Conference on Population and Development (United Nations
Population Information Network & United Nations Population Fund, 1996), the UN
delineated five major components of empowerment:
1. Women’s sense of self-worth
2. Women’s right to have and to determine choices
3. Women’s right to have access to opportunities and resources
4. Women’s right to have the power to control their own lives, both within and
outside the home
5. Women’s ability to influence the direction of social change to create a more
just social and economic order, nationally and internationally.
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One of the more comprehensive and broadly cited definitions of empowerment
comes from a study by Kabeer (1999, p. 437) who states that empowerment is “the
expansion in people’s ability to make strategic life choices in a context where this
ability was previously denied to them; a process that entails thinking outside the
system and challenging the status quo, where people can make choices from the
vantage point of real alternatives without punishingly high costs.” This definition is
reflected in our theory of change underlying economic SHGs, which includes
resources (for example, increased income, savings, and loan repayments), agency
(for example, increased autonomy, self-confidence, or self-efficacy), and
achievements (for example, ability to transform choices into desired action and
opportunities) (Kabeer, 1999). We based our review on the theory of change
underlying economic SHGs as depicted in Figure 1.1.
Figure 1.1: Economic self-help groups and empowerment causal pathway Source: authors.
Based on the literature, we hypothesized that women’s participation in economic
and livelihood SHGs would enable women to gain access to resources in the form of
credit, training, loans, or capital. As a result, women SHG members might
experience an increase in income, savings, and/or loan repayments. In addition,
participants would be exposed to group support. As a result of group support,
women SHG members might experience increased feelings of autonomy, self-
confidence, and self-efficacy. Following increased financial stability and self-
confidence, women SHG members might then be able to make meaningful life
choices, and their patterns of spending and savings might change. As a result of
these changes, women SHG members might experience an increased ability to
transform their choices into desired actions, which would lead to the emergence of
economic, political, social, and psychological empowerment (Eyben, Kabeer &
Cornwall, 2008). The potential for these changes to occur are dependent upon
“context, commitment and capacity” (Kabeer, 2005).
Empowerment studies have lent credence to the concept that women can and should
be central actors in social and economic development, but empowerment of an
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individual or a small group alone might invoke negative reactions when familial,
community, and structural factors have not yet adjusted to women’s changing roles.
Intimate partner violence, for example, has been shown to increase when women’s
economic empowerment is not complemented with additional interventions that
focus on mitigating the potential adverse consequences at the household and
community level (Ahmed, 2005; Dalal, 2011). Thus, several studies recommend
complementing interventions with an emphasis on empowering women with
interventions that focus on changing the gender norms of men (for example, Barker
& Schulte, 2010; Dworkin et al., 2011; Dworkin, Forthcoming).
Studies also suggest that increasing women’s monetary contributions to the family
without also taking into account the upheaval this might cause with respect to
expected gender and domestic responsibilities can lead to increased household and
community tensions and decreased emotional well-being for women (Ahmed 2005;
Ahmed & Chowdhury, 2001; De Hoop et al., 2014). Short- and long-term backlash
tendencies are, therefore, important to consider when examining the impacts of
SHGs on empowerment.
Numerous factors can modify the pathways described here. For example, the
literature highlights that empowerment can occur at the individual and collective
levels (Eyben, Kabeer & Cornwall, 2008). Individual empowerment refers to
changes that occur within an individual. Collective empowerment refers to
structural changes at the societal level in terms of how relationships and institutions
impact households and individuals. Although SHG participation might lead to
improved self-efficacy of an individual (individual empowerment), the systematic
marginalization of the group might remain unchanged (collective empowerment).
Hence, individual empowerment does not necessarily result in collective
empowerment. The economic climate, program fidelity, role of the facilitator, and
underlying race, ethnicity, class and/or caste issues can also affect how program
benefits are realized.
1.4 WHY IT IS IMPORTANT TO DO THIS REVIEW
Today, women’s empowerment is considered an essential component of
international development and poverty reduction. The concept of women’s
empowerment has gained increased attention over the past two decades. This
concept first held international prominence at the International Conference on
Population and Development in Cairo in 1994 and then again at the Fourth World
Conference on Women, Beijing, 1995. But the central role of women in development
originated during grassroots movements that commenced years earlier.
The international conferences at Cairo and Beijing announced the shift from
thinking of women as targets for fertility control policies to acknowledging women
as autonomous agents with rights. As a result of these conferences, a broad
assessment of women’s empowerment throughout the United Nations (UN) system
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was undertaken. By 2000, when 189 UN member states created eight poverty
reduction targets called the Millennium Development Goals, they agreed that
“promoting gender equality and empowering women” deserved to be included as a
stand-alone goal in addition to the other health and education-related targets
(UNDP, 2010). In addition, the UN now assesses the different implications of
development planning for women and men and integrates poverty eradication
strategies into programs for women (African Women’s Development and
Communication Network, 2010).
The international conferences at Cairo and Beijing helped shift resources and
ideologies toward women’s role in development, but the emergence of women’s
empowerment as a central concept in development was the result of earlier
grassroots movements aimed at empowering disenfranchised communities with
women playing a central role. Grassroots organizing included the formation of
SHGs, which became a central ground for women’s activism and participation and
helped to shape the changing development landscape in South Asia. Nowadays
SHGs are among the most popular programs that aim to stimulate the
empowerment of women in South Asia (Jakimow & Kilby, 2006). Although SHGs
have a less prominent history in low-and middle-income countries outside South
Asia, the formation of SHGs has also diffused to countries in other parts of Asia,
Sub-Saharan Africa, and Latin America.
The concept of the SHG as a catalyst for change in developing countries was based
on the self-help approach pioneered in India in the early 1980s. It emphasized high
levels of group ownership, control, and management concerning goals, processes,
and outcomes. It has been argued that the very process of making decisions within
the group is an empowering process and can lead to broader development outcomes
such as the greater participation of women in local governance and community
structures (Mayoux, 1998). For example, in case studies of women’s cooperatives in
rural Nigeria and rural India, women who were engaged in cooperative activities
appeared to be more productive and had higher levels of economic well-being, than
non-members (Amaza, Kwagbe & Amos, 1999; Datta & Gailey, 2012).
As these smaller SHGs became successful, larger umbrella organizations emerged
with the goal of harnessing the energy of smaller groups and advocating for the
rights of the poor and of women on the global stage. One example of an umbrella
organization is the Self Employment Women’s Association (SEWA), which was
launched in the state of Gujarat, India, by female garment workers, who first met in
a park to discuss their working conditions and eventually organized into a trade
union. This project, which was launched in 1972, has included thousands of women
and their families (Narayan et al., 2000).
Following the global recognition of the critical role of women in poverty reduction
strategies, a wave of microfinance programs and other livelihood support
interventions were implemented worldwide, specifically targeting rural women and
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women’s SHGs. As discussed above, a large majority of these programs focus
explicitly on empowerment, although the emphasis is sometimes on resolving
market failures.
We based our review on the understanding that a great deal of evidence about
women’s SHGs has already been generated from quantitative and qualitative
research, much of which might be useful in informing policy and practice.
Several systematic reviews focus on the impact of microfinance on economic well-
being. First, Duvendack and colleagues (2011) reviewed the evidence of the impact of
microfinance on the well-being of poor people. The authors found only limited
evidence that microfinance improves economic well-being, but felt limited by the
lack of rigorous impact evaluations on microfinance. Second, a systematic review by
Stewart and colleagues (2010) on the impact of microfinance on poor people in Sub-
Saharan Africa came to similar conclusions with respect to microcredit. The authors
concluded, however, that based on the evidence they included in their review, micro-
savings appeared to be more effective in improving the well-being of poor people.
Following this conclusion, the authors called for more rigorous evidence on the
impact of microsavings programs. Third, Stewart and colleagues (2011) reviewed
whether microcredit, microsavings, and microleasing serve as effective financial
inclusion interventions enabling poor people, and especially women, to engage in
meaningful economic opportunities in low- and middle-income countries. The
authors found mixed results once again. In some cases, microcredit and
microsavings reduced poverty but not in all circumstances or for all clients. The
authors also showed that there was not enough evidence to say that microfinance
interventions targeting women exclusively were more successful at reducing poverty
than those targeting both men and women.
The findings of these reviews stand in stark contrast to the prevailing positive view
about the impact of microfinance on poverty reduction before these reviews and a
number of randomized controlled trials (RCTs) were conducted. The prevailing
positive view was mostly based on anecdotal evidence and studies that were
vulnerable to selection bias (Roodman, 2011). Both donor and nongovernmental
organizations promoted microfinance on the basis of an understanding that it
reduced poverty and empowered women (White & Waddington, 2012). However,
new rigorous evidence from RCTs on the impacts of microcredit on poverty
reduction and women’s empowerment suggests that the effectiveness of microcredit
is at best modest (Attanasio et al., 2015; Augsburg et al., 2015; Banerjee et al., 2015;
Crepon et al., 2015).
A recent systematic review on the impact of microcredit on women’s bargaining
power also suggests that the prevailing positive view on the effects of microcredit on
women’s empowerment might be overstated (Vaessen et al., 2014). The evidence
from the most rigorous studies in that review, including those based on RCTs and
16 The Campbell Collaboration | www.campbellcollaboration.org
credible quasi-experiments, suggested there was no evidence for a causal link
between microcredit and women’s control over household spending.
There are, however, several mechanisms through which SHGs can improve women’s
empowerment. Apart from the economic channel, it is also important to focus on the
potential effects that group-support and training might have on women’s
empowerment. We focused on both of these mechanisms in the theory of change
described above.
The reviews cited previously were restricted to microcredit and microsavings
interventions and did not comprehensively review and synthesize the evidence on
the impact of SHGs that included collective finance, enterprise, and/or livelihoods
components. In addition, the reviews did not comprehensively cover a range of key
empowerment outcomes such as decision making within households, feelings of self-
confidence or autonomy, or the ability to exercise control over family planning.
Although Vaessen et al.’s review is the only one with an explicit focus on women’s
empowerment, the review does not focus exclusively on SHGs, covers only
microcredit interventions, and does not synthesize empowerment outcomes other
than women’s control over household resources.
The current review focuses on quantitative studies evaluating the impact of SHGs
with a broad range of collective finance, enterprise, and livelihood components on
political, economic, social, and psychological empowerment in addition to women’s
control over household resources. This systematic review thus goes beyond
determining the effects of microcredit on women’s empowerment to ensure that we
learn about the credit, saving, group support, and training components of women’s
SHGs.
In order to identify some of the pathways and moderators, we also included
qualitative studies of women’s perceptions of the barriers and facilitators to
women’s empowerment within SHGs. We recognize that heterogeneity in the design
and implementation of SHGs makes it difficult to interpret the existing evidence on
the impact of SHGs on women’s empowerment. Our systematic review assesses the
effects of women’s SHGs and the pathways and moderators to explain these effects
by using a mixed-methods evidence synthesis as in the systematic review on the
effects of farmer field schools (Waddington et al., 2014).
The protocol of this study is available through the Campbell Collaboration Library of
Systematic Reviews (Brody et al., 2014).
17 The Campbell Collaboration | www.campbellcollaboration.org
2 Objectives of the review
The primary objective of this review is to examine the impact of women’s economic
SHGs on individual-level empowerment for women in low- and middle-income
countries, using evidence from rigorous quantitative impact evaluations (review
objective 1).
The secondary objective of this review is to examine the perspectives of female
participants on factors determining their participation in, and benefits from,
economic SHGs in low- and middle-income countries using evidence from high-
quality qualitative evaluations (review objective 2).
Finally, this review aims to refine the theory of change introduced in section 1 that
describes how women’s economically oriented SHGs lead to women’s empowerment
using evidence drawn from both rigorous quantitative impact evaluation studies and
qualitative studies about perspectives of women who are SHGs participants.
18 The Campbell Collaboration | www.campbellcollaboration.org
3 Methods
3.1 CRITERIA FOR INCLUDING STUDIES IN THE REVIEW
We conducted an integrated mixed-methods review that examines data generated
through both quantitative and qualitative research methods. We believe this study
design will enhance the review’s utility and impact for practitioners and
policymakers. This approach allowed us to capture a broader range of evidence than
a review of quantitative studies alone so that we could answer relevant policy
questions more comprehensively.
We included studies in the review that fulfilled the following criteria.
3.1.1 Participants
SHG participants included women of all ages in low- and middle-income countries,
as defined by the World Bank categorization of low- and middle-income countries,
at the time the data were collected. Women’s SHGs and SHGs in which participation
was either limited exclusively to women or, if this was not the case, in which impacts
on women were assessed separately from men, were included. In contrast, studies
were excluded in which impacts were not disaggregated by gender and/or self-help
groups were comprised exclusively of men.
3.1.2 Interventions: type of women’s self-help group programs
We included studies on SHGs in which female participants physically came together
and received a collective finance and enterprise and/or livelihoods group
intervention:
We defined SHGs, also known as mutual aid or support groups, as those
groups that involved people who provide support for each other and/or are
created with the underlying assumption that when individuals join together
to take action toward overcoming obstacles and attaining social change,
individual, and/or collective empowerment can result.
We planned to examine those groups that were initiated by an external
agency (that is, a development organization or research group) as well as
those that had come into existence without any direct external involvement.
In practice, however, all included studies focused on groups that were
initiated by an external agency.
19 The Campbell Collaboration | www.campbellcollaboration.org
SHGs needed to receive an economic intervention that included or contained
the following components: collective finance and enterprise1 (such as savings
and loans, group credit, collective income-generation, micro-insurance)
and/or livelihoods interventions (such as life skills, capacity-building,
business training, financial education, labor and trade group organizing).2
We excluded studies evaluating individual self-help or group programs that
were not explicitly designed as self-help programs or did not have a collective
finance, enterprise, or livelihoods intervention component.
3.1.3 Outcomes
Primary outcomes
To be included in the review, studies had to measure at least one of the following
empowerment outcomes.3
Economic empowerment: We defined women’s economic empowerment as the
ability of women to access, own, and control resources. It could be measured in a
variety of ways, using outcome indicators such as income generation by women,
female ownership of assets and land, expenditure patterns, degree of women
participation in paid employment, division of domestic labor across men and
women, and control over financial decision making by women.
Political empowerment: We defined political empowerment as the ability to
participate in decision making focused on access to resources, rights, and
entitlements within communities. It could be measured using indicators such as
awareness of rights or laws, political participation such as voting, the ability to own
land legally, the ability to inherit property legally, and the ability to gain leadership
positions in the government.
Social empowerment: We defined social empowerment as the ability to exert
control over decision making within the household. Measures included women’s
mobility or freedom of movement, freedom from violence, negotiations and
discussion around sex, women’s control over choosing a spouse, women’s control
over age at marriage, women’s control over family size decision making, and
women’s access to education.
1 An example of a collective finance intervention is SaveAct in South Africa, which allows members of the community to voluntarily form a group and save money in the form of share purchases. The group also contributes monthly to a Social Fund to assist members in times of emergency or family crisis, such as a death in a member’s family (SaveAct.org, 2013). 2 An example of an individual livelihoods intervention was the Neang Kongrey Stoves project in
Cambodia, which offered training program to three groups of local potter women on how to produce
improved cook stoves (World Bank, 2009). 3 Sources: Malhotra, Schuler & Boender, 2002; Mayoux, 1998; Eyben, Kabeer & Cornwall, 2008.
20 The Campbell Collaboration | www.campbellcollaboration.org
Psychological empowerment: We defined psychological empowerment as the ability
to make choices and act on them. It could be measured using outcome indicators
such as self-efficacy or agency; feelings of autonomy; and sense of self-worth, self-
confidence, or self-esteem.
The definition of the outcome measures shows that empowerment is a broad
concept even when we divide it into four empowerment constructs. Furthermore,
study authors of primary studies use a large number of different operational
definitions to measure economic, social, political, and psychological empowerment.
The large number of outcome measures to operationalize empowerment is not
surprising, since the concept is difficult to define. Nonetheless, we had to be careful
in grouping outcome variables when we were not certain whether these outcome
variables measured the same construct. At the same time, the literature on
measuring empowerment suggests that empowerment should be considered a latent
construct that cannot be measured using one specific outcome variable. Thus,
several researchers use an index to measure empowerment (Pitt, Khandker &
Cartwright, 2006; Bali Swain & Wallentin, 2009). These indices suggest that
different operational definitions to measure empowerment can be considered part of
the same construct. For example, several studies construct indices based on
variables that measure different elements of women’s bargaining power, mobility,
family-size decision-making, and political, as well as psychological empowerment
(Pitt et al., 2006; Bali Swain & Wallentin, 2009). Nonetheless, we took seriously the
concern that different operational definitions of empowerment cannot always be
considered part of the same construct. Thus, we used an iterative approach in the
definition of our outcome measures. First, we grouped outcome variables under
economic, social, psychological, and political empowerment. Second, we synthesized
the evidence on the effects of women’s SHGs on these four constructs of women’s
empowerment under the assumption that it is appropriate to group the outcome
variables under the same construct. Third, we analyzed the robustness of the results
to excluding studies with outcome measures that might not measure the same
construct as the other outcome variables.
Secondary outcomes
We also examined spillover effects from women’s SHG participants to
nonparticipating women in the same communities on the same outcomes.
In addition, we examined adverse outcomes including:
Intimate partner violence.
Stigma.
Disappointment.
Reduced subjective well-being.
21 The Campbell Collaboration | www.campbellcollaboration.org
3.1.4 Study types
To answer our review questions, we included studies with study designs and
methods of analysis appropriate to each review objective.
Review objective 1: quantitative studies
We included the following study designs: 1) experimental designs using random
assignment to the intervention and 2) quasi-experimental designs with non-random
assignment (such as regression discontinuity designs, “natural experiments,” and
studies in which participants self-select into the program). To be included, the
studies needed to 1) collect data at baseline and endline (longitudinal) and/or cross-
sectional (endline) data from treatment and comparison groups; and 2) use
propensity score or other type of matching, difference-in differences estimation,
instrumental variables regression, multivariate cross-sectional regression analysis;
or other forms of multivariate analysis (such as the Heckman selection model or
multivariate ordinary least squares (OLS) regression analysis) that are able to
correct for selection bias under specific circumstances. We included studies in which
data were collected at the individual and/or group level. For studies that utilized
interrupted time series, at least three data points needed to be collected before and
after the intervention for the study to be included. Eligible comparison conditions
were no intervention, pipeline, or “business as usual.” We also included studies in
which the outcomes of SHG members, who were member for a short amount of
time, as defined by the researchers, were used as a comparison condition and/or
used the time of participation in the SHG as the treatment variable. However, we
were not able to include three studies that used time as a continuous explanatory
variable in the meta-analysis because these studies did not allow for estimating the
average impact of SHGs regardless of the time the women were members of the
SHGs. We did, however, analyze the results of these studies in a narrative manner.
Studies without any type of control or comparison group as outlined were excluded,
including single group pre-post studies which are likely to provide biased estimates
of effects due to confounding.
Review objective 2: qualitative studies
We included qualitative studies that explored empowerment from the perspectives
of women participants in SHGs using the following methodologies: in-depth
interviews, ethnography, participant observation, and focus groups. These studies
needed to mention an underlying analytical methodology such as phenomenological
analysis or grounded theory, report actual narratives from women reported as
direct quotations, and include discussion of factors that determined women’s
participation in, and benefits from, economic SHGs. Qualitative studies that did not
employ the defined methodologies listed previously and that did not draw from
direct quotations from female SHG participants were excluded.
22 The Campbell Collaboration | www.campbellcollaboration.org
3.1.5 Other study characteristics
To ensure that we included all studies since the emergence of SHGs in the early
1980s, studies were eligible which reported in any language and were conducted
between 1980 and February 2014. We excluded studies that were not conducted
within this time frame, with the exception of studies that were published if we had
already included the working paper on which the published paper was based
(Banerjee et al., 2015; De Hoop et al., 2014; Deininger & Liu, 2013).
3.2 SEARCH METHODS FOR IDENTIFICATION OF STUDIES
3.2.1 Electronic searches
To guide this search, we consulted an information retrieval specialist. This person is
the Cochrane specialist of a research group at a large university. She gave us
guidance on both search sources and search terms and built our Pubmed search
strategy (below) which we used to develop all subsequent search strategies. The
strategy was used to search for both qualitative and quantitative studies.
The literature search for the qualitative and qualitative studies were conducted
together and this search occurred in two phases.
Phase 1: The first phase involved searching the following databases:
PubMed (http://www.pubmed.gov)
IndMed (http://medind.nic.in/)
POPLINE (http://www.popline.org/)
PsycINFO (http://www.apa.org/psycinfo/)
Index Medicus for the WHO (http://www.globalhealthlibrary.net)
Social Sciences Citation Index (http://thomsonreuters.com/social-sciences-citation-
index/)
International Bibliography of the Social Sciences
(www.lse.ac.uk/collections/IBSS/about/keyFacts.htm)
British Library of Development Studies (BLDS) (http://blds.ids.ac.uk/)
Joint libraries of WB and IMF (JOLIS)
(http://external.worldbankimflib.org/external.htm)
3ie Database of Impact Evaluations (http://www.3ieimpact.org/evidence/impact-
evaluations/)
Econlit (https://www.aeaweb.org/econlit/)
Global Health (CABI) (http://www.cabi.org/publishing-products/online-
information-resources/global-health/)
Africabib (http://www.africabib.org/)
Phase 2: Phase two consisted of reviewing reference lists of included studies and
searching through studies that cited the included studies for additional resources,
23 The Campbell Collaboration | www.campbellcollaboration.org
conducting supplemental keyword searches using identified program names and
locations, and contacting key experts through an online survey for additional
information.
In the second phase of the search, we also conducted a supplemental keyword search
in Google.com based on leads generated by the search described above. For example,
if a search identified an article mentioning (but not evaluating) a self-help group
program through an MFI institution in the Philippines called Tulay sa Pag-unlad,
Inc. (TSPI), a search of Google.com and Google.scholar used a search of “Tulay sa
Pag-unlad Inc” and several keywords to determine whether there was additional
information on the program that might include evaluation information relevant to
the analysis.
When we encountered studies that were not in English, we reviewed the English
translation of abstracts that were available. We did not encounter any studies that
did not have abstracts available in English. No non-English studies that had English
abstracts met the inclusion criteria and therefore no further translation was needed.
We also searched the gray literature for dissertations, theses, government reports,
nongovernmental organization reports, and funder reports using the following
search engines and dissertations and theses.
Search engines:
IDEAS/RePEc
Google Scholar
Africa-Wide
Dissertations and theses:
ProQuest (http://www.umi.com/enUS/catalogs/databases/detail/pqdt.shtml)
Index to Theses (http://www.theses.com/)
Deutsche Nationalbibliothek (http://www.dissonline.de/)
We reviewed the results from these additional search engines, dissertations and
theses up to 100 hits ordered by relevance since we found no relevant studies when
scanning titles beyond this point.
3.2.2 Other searches
We electronically searched the collections from UC Berkeley Library and Touro
University California.
We hand-searched the following key journals (specifically the past two years in case
they had not been indexed in databases):
Current Anthropology, Development, Development and Change, Economic
Development and Cultural Change, Feminist Economics, Global Public Health,
24 The Campbell Collaboration | www.campbellcollaboration.org
Health Care for Women International, Health Policy, Health Policy and Planning,
Indian Growth and Development Review, Indian Journal of Gender Studies,
International Journal of Health Planning and Management, International Journal
of Sustainable Development, International Journal of Social Research
Methodology, Journal of Development Effectiveness, Journal of Development
Economics, Journal of International Development, Qualitative Research in
Psychology, Third World Quarterly, World Development.
We searched for relevant reports from the following multilateral organizations:
African Development Bank, Asian Development Bank, Inter-American Development
Bank, International Fund for Agricultural Development, United Kingdom’s
Department for International Development, United Nations Children’s Fund, United
Nations Development Fund for Women, United Nations Development Program,
United Nations Fund for Population, United States Agency for International
Development, World Bank, World Health Organization.
We also contacted key personnel at the following organizations and foundations to
elicit additional gray or unpublished information:
AED Center for Gender Equality, African Women’s Development and
Communication Network (FEMNET), Asian Women’s Network on Gender and
Development, the Center for the Evaluation of Global Action and Ford Foundation,
Global Fund for Women, GROOTS International, The Guttmacher Institute, The
Hewlett Foundation, International Committee for Research on Women, Latin
American Women and Habitat Network, The Packard Foundation, SEWA, UCGHI
Center of Expertise on Women’s Health and Empowerment, Women Deliver.
3.2.3 Search terms
The search strategy was used to search databases and was adjusted to fit the
diversity of search options available for each database. After discussion and
consultation with content experts and search strategists, we included general
keywords for the “exposure” and the “outcome” in our search strategy. The labeling
of self-help group participation as empowering had to come from the primary
researchers. We believe this strategy more accurately represented the evidence base
on the impact of self-help groups on empowerment and reduced misclassification
bias of our outcomes because it excluded studies in which outcome indicators did
not reflect empowerment according to the group and participants under study. This
decision excluded studies if these studies did not include somewhere in their text the
terms “empowerment,” “power,” or “control.” Our hand-searches and key informant
contributions did not produce any additional studies that did not include at least one
of these words. Thus, we are confident that our search strategy did not miss any
major studies that would have been included without the exclusion criteria
concerned with the terms “empowerment,” “power,” or “control”. The search
strategy was based on several consultations and discussions with our information
25 The Campbell Collaboration | www.campbellcollaboration.org
retrieval specialist. Truncated terms and stem-words were also used where
appropriate as shown in the example below.
An example of our search strategy that was used to search the PubMed database is as
follows:
Search Query Items Found
#5 Search ((#1) AND #2) AND #3 Filters: Publication date from 1980/01/01 to 2013/12/31
1741
#4 Search ((#1) AND #2) AND #3 1811
#3 Search “women’s self-help”[tiab] OR “women’s cooperative*”[tiab] OR “self-help group*”[tiab] OR “self help group*”[tiab] OR “support group*”[tiab] OR “lending group*”[tiab] OR “advocacy group*”[tiab] OR “micro finance”[tiab] OR “micro credit”[tiab] OR “microfinance”[tiab] OR “microcredit”[tiab] OR “income generation group*”[tiab] OR “microenterprise group*”[tiab] OR sangha[tiab] OR “Self-Help Groups”[Mesh] OR (women*[tiab] AND (finance*[tiab] OR economic*[tiab])) OR (“Women”[Mesh] AND (“Financing, Organized”[Mesh] OR “Economics”[Mesh]))
29946
#2 Search “women’s empowerment”[tiab] OR “empower*” [tiab] OR “girl’s empowerment”[tiab] OR “empowering”[tiab] OR “power”[tiab] OR “control”[tiab] OR “Power (Psychology)”[Mesh]
1743835
#1 Search (“developing country” [tiab] OR “developing countries” [tiab] OR “developing nation”[tiab] OR “developing nations”[tiab] OR “developing population”[tiab] OR "developing populations"[tiab] OR "developing world”[tiab] OR “less developed country”[tiab] OR “less developed countries”[tiab] OR “less developed nation”[tiab]… [and each individual LMICs; see Appendix 2 for full list]
1139069
3.3 DATA COLLECTION AND ANALYSIS
3.3.1 Selection of studies
In the first stage, two team members independently reviewed titles and abstracts or
executive summaries (where available) and excluded all references that were not
relevant. Disagreements about inclusion were resolved through discussion. A third
independent member of the team was used to resolve disagreement between the
reviewers’ conclusions.
In the second stage, two team members worked independently to apply the specified
inclusion criteria to the remaining full-text studies to determine whether the study
should be included for analysis. Discrepancies between the two reviewers’
assessments were reviewed by a senior team member for a decision.
The full text of each study was preliminarily assessed for full-text review. These
studies were retrieved and read in detail. They were screened again by four different
reviewers.
26 The Campbell Collaboration | www.campbellcollaboration.org
3.3.2 Data extraction and management
Two team members working independently extracted information from each
quantitative or qualitative study included in the review. Both team members used a
pre-piloted data extraction form and the data were summarized in a table.
Disagreements in coding were resolved through discussion. Study-, group-,
outcome-, and effect-level data extraction and coding forms guided the data
extraction (Appendix 1: Data extraction form).
3.3.3 Assessment of risk of bias in included studies
Review objective 1: quantitative studies
Two independent reviewers assessed the quantitative studies for rigor using an
adaptation of a set of criteria, developed by 3ie, to assess risk of bias in experimental
and quasi-experimental studies (Hombrados & Waddington, 2012). The critical
appraisal tool assessed the likely risk of the following biases:
1. Selection bias and confounding, based on quality of attribution methods
(mechanisms of assignment/identification), and assessment of group
equivalence
2. Performance bias, based on the extent of spillovers to women in comparison
groups
3. Outcome and analysis reporting biases
4. Other biases, including
a. Detection bias and placebo effects
b. Motivation and courtesy biases (Hawthorn effect and John Henry effect)
c. Coherence of results
d. Retrospective baseline data collection
e. Other biases, such as strong researcher involvement in the
implementation of the intervention and the use of cash transfers as a
compensating mechanism to participate in an intervention
The risk of bias assessment tool can be found in Appendix 6. We judged whether a
study was subject to high, medium or low risk of bias for each of these categories.
We reread studies several times if something was unclear and maximized the use of
all the available information from the studies. We based our assessments on the
reporting in individual papers, erring on the side of caution. For example, in those
cases in which the selection of participants was not clear, we classified the study as
being of high risk of selection bias. In all cases where the risk of bias was unclear we
assumed this was an indication of a high risk of bias.
We reported risk of bias assessment for each included study, conducting sensitivity
analyses in the meta-analysis by each risk of bias domain. For example, we
conducted meta-regressions to assess whether there were either substantive or
27 The Campbell Collaboration | www.campbellcollaboration.org
statistically significant differences between low, medium, and high risks of selection
bias and confounding, performance bias, outcome and analysis reporting bias, and
other biases. Based on these analyses, we then determined our preferred
specification for the meta-analysis. An overview of risk of bias assessment of
included effectiveness studies by risk of bias category and by category of bias can be
found in Appendix 7 and Appendix 8.
Review objective 2: qualitative studies
We assessed the quality of included studies using the 9-item Critical Appraisal Skills
Programme Qualitative Research Checklist (CASP, 2013), making judgments on the
adequacy of stated aims, the data collection methods, the analysis, the ethical
considerations and the conclusions drawn. The full checklist can be found in
Appendix 5. For each item, 2 researchers determined whether the study had
adequately met the item or not and gave “yes,” no,” or “can’t tell” responses. If
researchers disagreed, they discussed the item until they reached consensus.
Studies that had 0-2 “no” or “can’t tell” responses were considered low risk of bias,
studies that had 3-5 “no” or “can’t tell” responses were considered medium risk of
bias and studies that had 6-9 “no” or “can’t tell” responses were considered high risk
of bias. An overview of risk of bias assessment of included qualitative studies by risk
of bias item can be found in Appendix 9.
3.3.4 Measures of treatment effects
We extracted information from each quantitative study to allow for the estimation of
standardized effect sizes across studies to the extent possible. In addition, we
calculated standard errors and 95 per cent confidence intervals if the information
from the studies allowed for this. We conducted the sample size calculations in a
consistent way to ensure comparability across studies.
The quantitative studies in our review showed substantial variation in the way they
measured empowerment, even in those cases in which the studies measured the
same construct. This variation was not surprising as there is no consensus as to how
to measure economic, psychological, social and/or political empowerment. As
discussed in our section on outcome measures, we used an iterative approach to
determine whether outcome measures should be considered part of the same
measurement construct. First, we grouped outcome variables under economic,
social, psychological, and political empowerment. Then we synthesized the evidence
based on this grouping. Finally, we conducted additional analyses to determine
whether the results are robust to excluding studies with outcome measures that
might not measure the same construct as the other outcome variables.
Because the studies measured empowerment in different ways, they also used
different measurement scales. Several studies used dichotomous variables to
measure empowerment, whereas other studies used continuous variables or indexes
to measure empowerment.
28 The Campbell Collaboration | www.campbellcollaboration.org
Because of the different measurement scales, we report two types of effect sizes:
1. Standardized mean differences (Hedges’ g).
2. Odds ratios.
First, we calculated the Hedges’ g sample-size-corrected standardized mean
differences (SMDs) for continuous outcome variables, which measure the effect size
in units of standard deviation of the outcome variable. Second, we calculated odds
ratios (ORs) for dichotomous outcome variables. The odds ratio is the ratio of the
odds of an event occurring in the group of beneficiaries to the odds of the same event
occurring in the comparison group (Bland & Altman, 2000). We converted the odds
ratios to log odds ratios and the log odds ratios to standardized mean differences in
order to make the effect sizes for continuous and dichotomous outcome variables
comparable to each other. We describe the procedure for calculating the effect sizes
in more detail in Appendix 10.
We converted all effect sizes to standardized mean differences to ensure we could
use studies with different measurement scales in the same analysis. We found it
appropriate to use dichotomous variables and continuous variables in the same
meta-analysis because, in our case, variables with different measurement scales
measured the same construct.
3.3.5 Methods for handing dependent effect sizes
We included only one effect size per study in a single meta-analysis. In one case,
information was presented about the effectiveness of the same program in South
Africa in two different studies. In that instance, we chose to extract effect sizes from
the study that presented the most recent information (Kim et al., 2009). A different
study from Ethiopia presented two impact estimates for two different regions. For
this study, we calculated a pooled summary effect size using a random effect meta-
analysis that included the two studies to prevent bias from dependency across the
two studies. We used a random effect model because the two regions in Ethiopia can
be regarded as two different contexts (Desai & Tarozzi, 2011). We included this
summary effect size in the final meta-analysis.
Where studies reported more than one effect size based on different statistical
methods we selected the effect size with the lowest risk of bias. We used this
methodology for a study in India in which the authors used both propensity score
matching and instrumental variable regression analysis to determine the impact of
the program (De Hoop et al., 2014). A priori it was not clear which method had the
lowest risk of bias. However, the effect size calculation clarified that the
instrumental variable regression method did not result in valid effect sizes because
predicted empowerment values fell outside the bandwidth of values from 0–1 for
dichotomous variables. Although the impact estimates from the instrumental
variable regression analysis study might have presented qualitatively interesting
29 The Campbell Collaboration | www.campbellcollaboration.org
findings, the instrumental variable linear probability model did not show unbiased
impact estimates. Hence, the risk of bias of the effect size was high. Therefore, we
chose to use the impact estimates from the propensity score matching model for this
study because we considered these impact estimates as medium risk of bias.
Other studies presented several impact estimates for different variables that could
be argued to measure the same construct. In those cases, we chose to use either the
variable that we considered the best approximation of the construct or a sample-size
weighted average to measure a “synthetic effect size.” For example, in the study of
Kim et al. (2009), we constructed a sample-size weighted average by estimating the
average impact on self-confidence and financial confidence for psychological
empowerment and on the challenging of gender norms and autonomy in decision
making for social empowerment. In these cases, we used the average values of the
standard errors (without weighing for the sample size) to estimate the pooled
standard deviation. Similarly, for the study by De Hoop et al. (2014), we chose to
calculate a sample-size weighted average for social empowerment by averaging the
effects on the women’s autonomy to go to the market without their husbands’
permission and the women’s autonomy to go to the doctor without their husbands’
permission.
3.3.6 Unit of analysis issues
Where the standard error did not take clustering of outcomes into account in the
estimation of standard errors (that is, where the outcome variables were likely to be
clustered at a higher level of aggregation than the individual or household level but
this was not taken into consideration in the estimation of the standard errors and
confidence intervals), we used adjusted standard errors. For these studies with a risk
of unit of analysis error, we applied corrections to the standard errors and
confidence intervals using the variance inflation factor (Higgins & Green, 2011):
𝑆𝐸𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑 = 𝑆𝐸𝑢𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑 × √(1 + (𝑚 − 1) × 𝐼𝐶𝐶)
Here, m is the number of observations per cluster and ICC is the intracluster
correlation coefficient.
For the ICC, we used estimated ICCs for empowerment outcomes from a primary
study in Odisha, India, that was also included in the systematic review (De Hoop et
al., 2014). These ICCs were likely to be similar to ICCs in other studies, taking into
consideration the large number of studies from India that we included in our
systematic review. We were able to obtain the original data from the study in Odisha
because one of our co-authors was also an author for this primary study (De Hoop et
al., 2014). From the study in Odisha, we estimated an average ICC of 0.057 for
empowerment outcomes (0.053 for social empowerment, 0.068 for psychological
empowerment, 0.017 for economic empowerment, and 0.088 for measures of
intimate partner violence). We used the average value of the ICC of 0.057 for the
correction of the standard errors of political empowerment outcomes for which we
did not have an estimate of the ICC. The other ICCs were used for the calculation of
30 The Campbell Collaboration | www.campbellcollaboration.org
standard errors of intimate partner violence and social, psychological, and economic
empowerment, respectively. If information about cluster size was not reported, we
estimated the cluster size by dividing the total number of participants in each
analysis (or the total number of participants if former not available) by the number
of clusters. We applied this methodology to correct standard errors for 9 included
studies (Ahmed, 2005; Mahmud, 1994; Nessa et al., 2012; Osmani, 2007; Rosenberg
et al., 2011; Sherman et al., 2010; Steel et al., 1998; Swendeman et al., 2009; Kim et
al., 2009).
3.3.7 Dealing with missing data
If the necessary data to calculate effect sizes were not available in the included
studies, we attempted to contact the authors of the studies. In those cases in which
we were not able to retrieve the missing data, we extracted or imputed effect sizes
and associated standard errors based on commonly reported statistics such as the t
or F statistic or p or z- values using David Wilson’s practical meta-analysis effect-
size calculator. Where studies did not report sample sizes for the treatment and the
control or comparison group, we assumed equal sample sizes across the groups.
We faced several challenges with missing data in the calculation of effect sizes. First,
the majority of studies that had a dichotomous dependent variable used a linear
probability model rather than a logit or probit regression to estimate the
effectiveness of self-help groups. Fortunately, empirically there are not many
differences in marginal effects between linear probability models and nonlinear logit
and probit models (Angrist & Pischke, 2009), which allowed us to estimate odds
ratios under the assumption of linearity in the estimation of the standardized effect
sizes. We applied this methodology to calculate effect sizes from linear probability
models for dichotomous outcome variables for several studies (De Hoop et al., 2014;
Desai & Tarozzi, 2011; Desai & Joshi, 2012).
Furthermore, a number of studies did not report the standard deviation of a
dichotomous outcome variable, but did report the full distribution of these variables.
We estimated the variance and standard deviation of these outcome variables based
on the full distribution of the dichotomous outcome variables. Thus, in cases where
studies reported sample sizes and the proportion of events and non-events in the
sample was available we calculated the standard deviation and the effect size based
on information about the sample sizes and the proportion of events and non-events.
We also included an effect size from an ordered probit regression model under the
assumption that the effect size would be approximately the same if the authors had
used an ordinary least squares regression model. We assumed that the point
31 The Campbell Collaboration | www.campbellcollaboration.org
estimate from the ordered probit model would give a good estimate of the mean
difference.4
In addition, a number of studies did not report the standard deviation of a
dichotomous outcome variable but did report the full distribution of these variables.
We were able to estimate the variance and standard deviation of outcome variables
for which the standard deviation was not reported but for which the full distribution
was reported. One study reported impact estimates using propensity score
matching, but estimating the effect size in the absence of information about the
standard deviation was not feasible (Deininger & Liu, 2013). In that specific case of a
study from India, we imputed the standard deviation for the dichotomous outcome
variables by replacing the missing standard deviations with standard deviations
from similar outcome variables that were used in other studies in India (Banerjee et
al., 2015; De Hoop et al., 2014).
In the absence of standard errors for the regression analysis, we also estimated the
standard error of the regression analysis using the degree to which the results were
statistically significant, with stars representing the significance level for one study
(Mahmud, 1994).
Following all the conversions, we were able to increase the number of studies in the
meta-analysis to 16 in total.
We were not able to include all studies in the meta-analysis. Two studies only
demonstrated whether results were significant without the associated point
estimates and standard errors. These studies did also not report t-statistics or p-
values so we were not able to estimate effect sizes (Husain, Mukherjee & Dutta,
2010; Mukherjee & Kundu, 2012). One other study showed separate time-trends for
latent outcome variables of the treatment and comparison group (Bali Swain &
Wallentin, 2009). But these time trends alone did not allow us to extract effect sizes
from the study, also because the latent variables were constructed separately for the
treatment and the comparison group. The latter raises significant concerns with
respect to the validity of the results. Finally, there were four studies that did not
assess the impact of SHG membership but did assess the relationship between the
time women were members of self-help groups (for example, in months) and
women’s empowerment (Coleman, 2002; Garikipati, 2008; Garikipati, 2012;
Holvoet, 2005). These studies did not allow for the estimation of the average impact
of women’s self-help groups on women’s empowerment. Nonetheless, we discuss the
results of the studies narratively in our quantitative synthesis.
4 Results were not sensitive to exclusion of this study.
32 The Campbell Collaboration | www.campbellcollaboration.org
In those cases in which we were not able to calculate the effect size, we contacted the
authors with a request for the necessary information to calculate the effect size.
3.3.8 Data synthesis
We conducted an integrated mixed-methods review in order to benefit from data
generated through both quantitative and qualitative research and to enhance the
review’s utility and impact for policymakers. An integrated review has three stages:
1) a synthesis of quantitative effects, 2) a synthesis of relevant qualitative evidence,
and 3) a synthesis of both summaries that “goes beyond” the primary studies and
generates new interpretations or hypotheses (Harden, 2010; Thomas et al., 2004).
We conducted a meta-analysis with the data extracted from quantitative studies, and
used meta-synthesis methods to synthesize the textual data extracted from the
qualitative studies. We then integrated the findings from the qualitative synthesis
with those from the quantitative studies to develop a framework for assessing how
economic self-help groups might impact women’s empowerment.
3.3.9 Quantitative synthesis
For our quantitative synthesis (review objective 1), we statistically combined the
effect sizes and associated standard errors from 23 quantitative studies that assessed
the impact of self-help group programs on women’s empowerment. We only
combined studies that focused on empowerment indicators that could be considered
sufficiently similar. Hence, we conducted a separate meta-analysis for studies that
focused on economic empowerment, social empowerment, psychological
empowerment, and political empowerment, respectively. We believe these different
empowerment indicators can be considered different constructs, so we did not
consider it appropriate to combine these empowerment indicators in one meta-
analysis.
We used inverse-variance weighted random-effects meta-analysis and used
established statistical techniques to analyze heterogeneity. We used random-effects
instead of fixed-effect analysis in order to allow for contextual and methodological
heterogeneity in the effect sizes.
With respect to spillovers, we were unfortunately not able to report and synthesize
effect sizes separately for women’s self-help group participants and neighboring
women who might indirectly benefit from the intervention. None of the included
studies separately reported these effect sizes.
3.3.10 Assessment of heterogeneity
We explored heterogeneity across studies with an emphasis on social and economic
empowerment using I-squared and Q as well as tau-squared and the visualization of
the forest plots (Borenstein et al., 2009). The results suggested there was
considerable heterogeneity in the effect sizes, although less so for impacts on
33 The Campbell Collaboration | www.campbellcollaboration.org
economic empowerment. This result was not surprising, since a substantial number
of existing studies argue there is significant heterogeneity in the effectiveness of
community-based programs, such as women’s self-help group interventions. This
heterogeneity could be related to several contextual characteristics, such as
diverging gender norms across contexts and differences in the capacity to implement
community-based programs (for example, De Hoop, 2012; Mansuri & Rao, 2004;
Woolcock, 2013).
It was not possible to explore heterogeneity in the impact of self-help groups on
political and psychological empowerment. The number of studies focusing on these
indicators was not sufficient for a reliable assessment of the heterogeneity in the
impact estimates, either with a meta-analysis or with a narrative synthesis.
3.3.11 Investigation of heterogeneous effects for subgroups
We also investigated factors explaining heterogeneity by using inverse-variance
weighted meta-regressions and stratified meta-analysis according to contextual and
methodological moderator variables. We used two contextual moderating variables:
type of intervention component; and geographic location.
We used a narrative synthesis to explore heterogeneity in the results for these
subgroups because our sample of studies was relatively small. For this analysis, we
integrated the findings of the qualitative analysis with the findings of the
quantitative analysis to the extent possible. Hence, the potential catalysts and
constraints toward the effectiveness of self-help groups that we present came from
both the quantitative and the qualitative studies.
3.3.12 Sensitivity analysis
We performed an extensive sensitivity analysis for two methodological effect size
moderators:
Risk of bias status for each risk of bias category (where sufficient studies
were available).
Study design (RCTs vs. quasi-experimental studies).
We used an iterative approach based on the risk of bias assessment discussed
previously to determine whether studies with different evaluation designs and
different outcome measures could be combined. First, we conducted stratified meta-
analyses for the randomized controlled trials and quasi-experimental evaluations in
our sample. Second, we conducted meta-analyses for experimental and quasi-
experimental studies with low, medium, and high risk of bias, respectively. Third, we
compared the effect sizes of the different analyses to determine whether studies
could potentially be combined into a single meta-analysis. In those cases in which
we were not certain whether we could combine studies in a single meta-analysis, we
conducted several meta-regressions to make decisions about combining studies with
34 The Campbell Collaboration | www.campbellcollaboration.org
different characteristics in one meta-analysis. We decided to combine studies in a
single analysis when the meta-regression did not show significant, either
substantively or statistically, differences in the effect sizes between the studies with
different risks of bias. In addition, we conducted robustness checks to determine
whether studies with different outcome measures that potentially measure different
empowerment constructs in the same empowerment domain (economic, social,
psychological, or political empowerment) could be combined with each other in a
single analysis.
We decided not to conduct meta-regressions with more than one explanatory
variable because of the relatively small number of studies. Instead, we chose an
iterative method in which we conducted several meta-regressions one by one to
determine whether the results from studies with different methodologies and
different risk of bias were sufficiently similar to combine in one meta-analysis. We
started with a meta-regression to determine whether studies with studies with a low
or high risk of selection bias were sufficiently similar to each other. Our approach
was such that when the meta-regression presented significant, either substantively
or statistically, differences between studies with a low and high risk of selection bias
we excluded studies with a high risk of selection bias from the analyses. But we kept
the studies with a high risk of selection bias in the analyses when the result did not
show substantive or statistically significant differences between studies with a high
and a low risk of selection bias. Then we continued with a meta-regression to
compare findings between randomized controlled trials and quasi-experimental
studies with a medium risk of bias (our synthesis did not include quasi-experimental
studies with a low risk of selection bias or RCTs with a high risk of selection-bias) to
see if there would be a difference between RCTs and quasi-experimental studies with
medium risk of selection-bias. Similarly, we excluded quasi-experimental studies
with medium risk of selection bias from the analyses if the meta-regression
suggested the findings of RCTs and quasi-experimental studies with a medium risk
of selection bias were significantly, either substantively or statistically, different. But
we combined the studies in a single meta-analysis if the findings of RCTs and quasi-
experimental studies with a medium risk of selection bias were not substantively or
significantly different from each other.
We used the same approach for different risks of bias (performance bias, outcome
reporting bias, and other biases) to arrive finally at a preferred specification with
randomized controlled trials with low risks of bias combined with randomized
controlled trials and quasi-experimental studies with a higher (medium or high) risk
of bias that did not show substantively or statistically significant different effects
from randomized controlled trials with a low risk of bias. But our approach was such
that we only combined RCTs with quasi-experimental studies that showed similar
results in one meta-analysis to account for the possibility of selection bias in quasi-
experimental studies.
35 The Campbell Collaboration | www.campbellcollaboration.org
We used a similar approach to determine whether studies with different outcome
measures to measure the same empowerment construct (economic, social, political,
and psychological empowerment) could be combined with each other in one meta-
analysis. For this decision we estimated meta-analysis with and without the study or
studies with a different outcome measure. We excluded studies with different
outcome measures from the meta-analysis or ran a separate meta-analysis if the
analysis without those studies showed substantively different effects from the
analysis with those studies.
In addition, we performed a sensitivity analysis to determine whether studies with
different outcome measures that could potentially measure different empowerment
constructs can be used in the same meta-analysis by running meta-analysis with and
without studies that use different outcome variables.
We did not conduct meta-regression analysis with more than one moderator
variable in our sensitivity analysis because of the relatively small number of
quantitative studies in our review.
3.3.13 Assessment of publication bias
We assessed the potential for publication bias using funnel plots for impact
estimates on economic and social empowerment. In addition, we conducted Egger’s
test. For psychological and political empowerment, our sample size was insufficient
for funnel plots to be informative about the potential for publication bias. Our
sample size for political and psychological empowerment was also not sufficient for
determining publication bias by comparing published with non-published studies.
3.3.14 Qualitative synthesis
The qualitative synthesis (review objective 2) was based on meta-ethnographic
techniques. This process was drawn from Atkins et al. (2008), Noblit and Hare
(1988) and Walsh and Downe (2005). Meta-ethnography is an interpretive approach
for combining the findings of qualitative research in order to provide a higher level
of analysis than individual studies alone.
Our qualitative synthesis provides a summary of women’s explanations of
empowerment outcomes as reported in the contributing studies. The manuscripts of
the included studies were first read and reread with special attention paid to themes,
quotations and authors’ interpretations of the quotations. Quotations from women
who discussed their experiences of empowerment were then identified and labeled
with respect to the topic or concept that they represented. All quotations that were
labeled or coded were subsequently categorized into empowerment themes. This
process included re-reading all labels or codes and deciding which codes were
important and how they related to each other. Codes that related to similar themes
were clustered together into categories that were also labeled. These categories or
themes are presented in the results section with example quotations as evidence, in
36 The Campbell Collaboration | www.campbellcollaboration.org
order to deepen readers understanding of the data. We used a systematic process to
select and synthesize representative quotes from women SHG members. The
selection of representative quotes was an iterative process in which two researchers
identified quotations and discussed emergent themes from the included studies and
determined how they were related, or dissonant, through a compare-and-contrast
exercise. Typically in qualitative research, authors report 1-2 example quotations but
we also provide additional quotations in Appendix 12 to improve readers’ sense of
the raw data and to demonstrate both the variability and the similarity between
studies. Reporting 1-2 example quotations may result in reporting bias due to
“cherry-picking” of non-representative quotations. Unfortunately, it is not feasible to
fully account for reporting bias in qualitative research. Nonetheless, our approach,
in which we provide additional representative quotes, mitigates some of the concern
regarding reporting bias.
In a summary table, each category is defined, two representative quotations are
given and the confidence in the findings for each category was assessed based on
three areas: 1) the risk of bias assessment of the contributing studies, 2) the
adequacy of the data and 3) the coherence of the theme that supported the finding.
The risk of bias for each of the contributing studies is reported in the summary table
based on the results of the CASP checklist as described earlier. Adequacy relates to
consideration of the thickness of data and the number of studies. Thick data is
achieved when detailed account of participants’ experiences make explicit the
phenomenon of interest. This is in contrast to a thin description, which is a more
superficial account. Coherence relates to the strength of the theme across settings
such as countries or regions. Based on an overall assessment of methodological
quality through the risk of bias, as well as the adequacy and coherence of the data,
the confidence in the evidence for each category was assessed as high, moderate, or
low by two researchers and if assessed differently, they discusses until consensus
was reached. A rationale with details about each confidence area is given in a
summary table. The process of assessment of confidence we use is in alignment with
the methodology used in Bohren et al. (2015).
3.3.15 Integrating findings from quantitative and qualitative syntheses
To integrate the findings from quantitative and qualitative synthesis, we conducted
the synthesis of effects along the causal chain of the theory of change (Figure 1.1)
and used the findings of the qualitative synthesis to “interrogate” and/or
complement the quantitative synthesis. The information from participants gathered
through qualitative investigations was used to understand whether and where any
causal chain links broke down. In other words, findings from the qualitative
synthesis helped describe, explore, and interpret both the nature of the
empowerment process and the extent to which women experienced empowerment
as recommended in the policies and guidelines of the Campbell Collaboration
(Campbell Systematic Reviews, 2014).
37 The Campbell Collaboration | www.campbellcollaboration.org
The mixed methods review allowed us to gather information using different
methodologies that informed, enhanced, and supplemented each other. The findings
from the integrated synthesis were used to revise and improve our theory of change.
We did this by using information extracted from the included studies and provided
insights about the nature and utility of the measures used to capture empowerment.
Our aim was to synthesize the evidence produced by both bodies of research to
capture the state of the evidence for the impact of self-help groups on women’s
empowerment.
3.4 DEVIATIONS FROM THE PROTOCOL
The review deviates from the proposed protocol in three respects.
First, we originally intended to exclude outcomes evaluating “women’s control over
household resources” from microcredit self-help group studies so as not to overlap
with an existing Campbell review on the impact of microcredit on women’s control
over household resources (Vaessen et al., 2014). However, that review does not focus
specifically on self-help groups, nor does it disaggregate findings for self-help group
participants and non-self-help group participants. At the same time, excluding
women’s control over household resources would have resulted in a considerable
omission of an important outcome and undermined the comprehensiveness and value
added of our review. For the sake of completeness, we have, therefore, decided to
include women’s control over household resources as a relevant outcome measure of
economic empowerment in our review.
Second, in our original study design inclusion criteria, we specified we would only
include those types of quasi-experimental studies that used statistical matching,
difference-in-differences estimation, instrumental variables regression, or other
forms of multivariate analysis (such as Heckman’s selection models) that correct for
selection bias. However, we decided also to include studies that used multivariate
cross-sectional regression analysis with a dummy variable for SHG participation as a
treatment variable. The identification strategy to determine causal effects of these
types of studies is usually not considered credible, which may result in high risk of
bias. Nevertheless, Pritchett and Sandefur (2013) proposed that including these types
of studies in a meta-analysis can increase the relevance of the meta-analysis because it
allows for the inclusion of studies in contexts without rigorous studies regarding the
specific topic. However, we protected internal validity by a strong focus on risk of bias
assessment and by conducting subgroup analyses for studies with a relatively low,
medium, or high risk of bias (Campbell Systematic Reviews, 2014). In these analyses,
we assessed all multivariate cross-sectional regression analysis with a dummy for
SHG participation as high risk of selection-bias in a meta-regression. We then
compared the estimates from studies with a high risk of selection-bias with the
estimates of studies with a low-or medium risk of selection-bias. Section 4 of this
systematic review shows that the findings of our review are sensitive to the inclusion
38 The Campbell Collaboration | www.campbellcollaboration.org
of multivariate cross-sectional regression analysis. Thus, we emphasize the findings of
studies with a low-or medium risk of selection-bias in the interpretation of our
results.
Finally, in the protocol, we proposed to provide an overall risk of bias classification for
each included study. However, to align with the most recent Campbell Collaboration
best practice, we avoided using an overall quality scale and instead used risk of bias
assessments for specific domains, such as selection bias and confounding,
performance bias, outcome and analysis reporting bias, and other biases. Evidence
suggests that assessments of overall risk of bias that do not take into consideration
specific domains are too dependent on the type of quality scale used and can
considerably influence the interpretation of meta-analysis results (Jüni et al., 1999).
This risk of randomness in the risk of bias assessment is most likely less severe when
risk of bias assessments focus on a specific domain, such as selection bias and
confounding, performance bias, outcome and analysis reporting bias, or other biases.
39 The Campbell Collaboration | www.campbellcollaboration.org
4 Results
4.1 RESULTS OF THE SEARCH
The search was conducted from March 2013–February 2014. We included a total of
23 quantitative and 11 qualitative studies in the final analysis. Figure 4.1 details the
flow diagram of the filtering process used to identify the final included studies.
Initially, we reviewed 3,536 abstracts from electronic database searches and 351
abstracts from the gray literature search (see Appendix 3). Of these, we excluded 38
duplicates and 3,133 irrelevant studies. We retrieved and reviewed the full text of the
remaining 365 studies using the predetermined criteria for inclusion. These studies
came from database searches including library catalogues (208), hand-searches of
websites (108), keyword searches (48), and author contacts (2).
Based on the full-text review of the 365 studies, we excluded 257 studies when
applying the criteria. There was 93 per cent agreement among reviewers. The
following were the main reasons for exclusion:
The study did not meet our criteria of an empirical evaluation (145).
The intervention under study did not meet our criteria of a women’s
economic self-help group (88).
The evaluation design did not employ appropriate methodologies (12).
The evaluation did not measure an empowerment outcome (7).
The study was not focused on a self-help group in a low- or middle-
income country (4).
40 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.1: Study search
We reviewed the remaining 109 full-text studies (55 quantitative, 36 qualitative, 18
mixed methods) again, with specific attention paid to the methods employed.
Through this process, we excluded another 74 studies because of the lack of a
comparison group, a lack of quantitative estimates of impacts, a lack of a use of
empowerment outcomes for quantitative studies, and a lack of data from direct
observation or a lack of reporting on individual narratives for qualitative studies.
Reasons for exclusion by study are reported in Appendix 4. The remaining 23
quantitative and 12 qualitative studies were included and used as the basis of the
analysis that follows. Most studies were identified through database searches and
came from peer-reviewed journals.
Records identified through database searching (n = 3536)
Additional records identified through other sources
(n = 350)
Records after duplicates removed (n = 3498)
Abstracts screened (n = 3498)
Records excluded (n = 3136)
Full-text articles assessed for eligibility (n = 362)
Full-text articles excluded, with reasons
(n = 257)
Studies included in qualitative synthesis
(n = 11)
Studies included in quantitative synthesis
(n = 23)
Refined screening of remaining 107 full-text
articles; 74 excluded with reasons
41 The Campbell Collaboration | www.campbellcollaboration.org
Table 4.1 summarizes the reasons for exclusion of marginal studies. The full
citations of excluded marginal studies are available in the reference section and a
full list of reasons for exclusion of marginal studies is available in Appendix 4. Table
4.2 summarizes sources and publication types for the included quantitative and
qualitative studies.
Table 4.1: Reasons for exclusion of studies
Quantitative Studies (n=33)
12 Study did not measure empowerment outcomes.
11 There was no comparison group.
7 Study did not evaluate a SHG.
4 There was no quantitative estimate of impact.
Qualitative Studies (n=46)
29 Study did not evaluate the effects of a SHG.
14 Study did not report any direct quotes from participants.
3 Study did not focus on empowerment outcomes.
Table 4.2: Sources and publication types for included studies
Source of Included Study Quantitative Qualitative
Database searches 12 6
Keyword searches 6 2
Hand-searching of organization websites
5 2
Library Catalogue -- 1
Key contact 1 --
TOTAL 23 11
Publication Type
Peer-reviewed journal 15 6
Unpublished report 8 1
Book -- 1
Dissertation -- 3
TOTAL 23 11
42 The Campbell Collaboration | www.campbellcollaboration.org
4.2 DESCRIPTION OF INCLUDED STUDIES
4.2.1 Quantitative studies (review objective 1)
The empowerment categories extracted from the quantitative studies were handled
in the following way:
Economic empowerment: We included all studies measuring indicators of
economic empowerment, but only meta-analyzed outcomes that focused on
decision-making by women in the household. For the other indicators, we did not
have a sufficient number of studies with outcome measures that were sufficiently
conceptually similar to perform meta-analysis. We report the effect size findings
narratively for these other indicators.
Political empowerment: We included all studies measuring indicators of
political empowerment and meta-analyzed outcomes that focused on political
participation, with an emphasis on voting. For the other indicators, we did not find
any rigorous studies.
Social empowerment: We included all studies measuring indicators of social
empowerment and meta-analyzed outcomes with an emphasis on mobility or
freedom of movement and control over family size decision making jointly and
separately. For the other indicators, we did not find an adequate number of studies
with outcome measures that were sufficiently conceptually similar to perform meta-
analysis. We report the effect size findings narratively for these other indicators.
Psychological empowerment: We included all studies measuring indicators of
psychological empowerment and meta-analyzed outcomes that focused on self-
confidence. For the other indicators, we did not find any studies.
All indicators were measured through household surveys, validated scales, and/or
structured closed-ended questionnaires. For example, Bali Swain & Wallentin
(2009) use a validated scale to measure a general empowerment index and Banerjee
et al. (2015) use a normalized index score to measure economic empowerment,
whereas Deininger and Liu (2013) and Holvoet (2005) and use several dummy
variables measured through a household survey to measure women’s economic and
social empowerment. De Hoop et al. (2014) use a 5-point Likert scale to measure
psychological empowerment, while Kim et al. (2009) use a dummy variable
indicating whether a respondent is self-confident to measure psychological
empowerment. Desai and Joshi (2012) also use several dummy variables indicating
women’s participation in community meetings and elections to measure political
empowerment.
Aggregate-level empowerment outcomes such as women’s right to vote, legislation
against domestic violence, inheritance law, female literacy, female child survival,
43 The Campbell Collaboration | www.campbellcollaboration.org
and so on, were excluded from this review, also because these indicators were not
clearly related to women’s SHGs.
We also examined spillover effects from women’s self-help group participants to
nonparticipating women in the same communities. Furthermore, we examined
adverse outcomes including intimate partner violence, stigma, disappointment and
reduced subjective well-being.
Table 4.3 summarizes data on the SHG name, country, type of training provided, the
outcome and methods used for the 23 included quantitative studies representing
data from 21 SHGs, predominately based in South Asia. Of the evaluated self-help
groups, 11 were implemented in India and 6 were implemented in Bangladesh. The
remaining studies came from Thailand (1), South Africa (1), Ethiopia (1), and Haiti
(1). Two self-help groups (one from India and one from South Africa) were discussed
in two quantitative papers. One study consisted of two separate analyses for samples
in two different regions in Ethiopia (Desai & Tarozzi, 2011). All of the study findings
were based on analyses of self-reported survey data either based on experimental or
observational designs.
Although in most cases detailed information on the intervention activities was not
recorded clearly, several studies present some information about whether any
training or services was offered to the SHG and the type of training offered. Ten of
the self-help groups did not report any additional training or services beyond
financial services (credit, loans, and savings). The remaining 11 groups offered some
combination of the following: health education (4), business or entrepreneurial
skills (6), awareness of women’s rights (2), basic education (2), and community-
development training (2). However, this list of training and supplemental activities
only represents what was reported by authors.
All of the included self-help groups were initiated by local or international NGOs
and community-based organizations. Four of the 20 groups were initiated by the
Grameen Bank and three by the Bangladesh Rural Advancement Committee
(BRAC). Only two of the groups, represented in three studies, were initiated as the
intervention arm of a research study (Pronyk et al., 2006; Kim et al., 2009; Sherman
et al., 2010).
The study designs and methods of analysis used in the studies were very diverse.
Four studies used cluster-randomized assignment (Desai & Joshi, 2012; Desai &
Tarozzi, 2011; Kim et al., 2009 (incorporating Pronyk et al., 2006); Sherman et al.,
2010). The remaining studies were based on observational data using methods of
counterfactual identification such as propensity score matching (PSM) (de Hoop et
al., 2014), PSM combined with double-differences (Deininger & Liu, 2009) and
instrumental variables analysis (Osmani, 2007; Pitt et al., 2006). Methods used to
estimate treatment effects ranged from ordinary least squares regression analysis
(for example, Osmani, 2007) and logistic regression (for example, Ahmed, 2005) to
44 The Campbell Collaboration | www.campbellcollaboration.org
the calculation of risk-or odds ratios based on events/non-events (for example,
Swendeman et al., 2009).
45 The Campbell Collaboration | www.campbellcollaboration.org
Table 4.3: Summary of included quantitative studies
Study SHG Name Setting Additional Training Outcome Sample Data Collection
Study design Analysis
Ahmed, 2005 BRAC Bangladesh Business Skills (1) Intimate Partner Violence
2044 Households with currently married women in 60 villages
Survey data Cross-sectional observational study
Logistic Regression
Banerjee et al., 2015 Spandana Hyderabad, India (South)
None (1) Economic Empowerment
1220 households in 52 randomly selected neighborhoods that are eligible for Spandana microfinance
Survey data Repeated cross-section cluster-randomized controlled trial
Linear probability model (OLS)
Bali Swain & Wallentin, 2009
SHG Bank Linkage Program
India (5 states) None (1) Empowerment Index
1000 households in 2 representative districts in 5 Indian states that were randomly selected from SHG members, and a comparison group that is comparable in terms of socio-economic characteristics
Survey data Observational study with panel data
Analysis of separate time trends for treatment and comparison households
Coleman, 2002 Bank for Agriculture and Agricultural Cooperatives
Thailand None (1) Economic Empowerment
445 households in 14 villages
Survey data Observational study with panel data
OLS regression analysis
46 The Campbell Collaboration | www.campbellcollaboration.org
Study SHG Name Setting Additional Training Outcome Sample Data Collection
Study design Analysis
De Hoop et al., 2014 CENDERET Orissa, India Business Skills, Rights Awareness
(1) Economic Empowerment (2) Social Empowerment (Mobility) (3) Psychological Empowerment (4) Intimate Partner Violence
398 households in 19 villages
Survey data Cross-sectional observational study
Propensity score matching
Deininger and Liu, 2009
Indhira Kranthi Patham Program
Andhra Pradesh, India (South)
Community Development
(1) Economic Empowerment (2) Social Empowerment (Mobility) (3) Political Empowerment
6340 households from 659 villages
Survey and census data
Observational study with cross-sectional and recall data
Double-difference propensity score matching
Desai and Joshi, 2012
Self-Employed Women’s Association (SEWA)
Rajasthan, India (North)
Business Skills , Child Care Services, Employment Training, Leadership Training
(1) Economic Empowerment (2) Social Empowerment (Family Size Decision Making) (3) Political Empowerment
3535 households from 82 villages
Survey data Repeated cross-section cluster-randomized controlled trial
Linear probability model with block-level fixed effects
Desai and Tarozzi, 2011
Amhara Development Association
Ethiopia Family Planning Services
(1) Social Empowerment (Family Size Decision Making)
1600 households in 54 villages
Survey Data Repeated cross-section cluster-randomized controlled trial
Linear probability model
47 The Campbell Collaboration | www.campbellcollaboration.org
Study SHG Name Setting Additional Training Outcome Sample Data Collection
Study design Analysis
Desai and Tarozzi, 2011
Oromia Credit and Savings Share Company, & Oromia Development Association
Ethiopia Family Planning Services
(1) Social Empowerment (Family Size Decision Making)
1600 households in 54 villages
Survey Data Repeated cross-section cluster-randomized controlled trial
Linear probability model
Garikipati, 2008 National Bank for Rural and Agricultural Development
Andhra Pradesh, India (South)
None (1) Economic Empowerment
291 households in 2 villages
Survey data Observational study with cross-sectional data
Instrumental variable regression analysis with time spent in SHG as explanatory variable
Garikipati, 2012 National Bank for Rural and Agricultural Development
Andhra Pradesh, India (South)
None (1) Economic Empowerment
291 households in 2 villages
Survey data Observational study with cross-sectional data
Instrumental variable regression analysis with time spent in SHG as explanatory variable
Holvoet, 2005 IRDP & TNWDP Tamil Nadu, India (South)
Rights Awareness, Community Development, Business Skills
(1) Economic Empowerment (2) Social Empowerment (Family Size Decision Making)
300 households in 6 blocks
Survey data Observational study with cross-sectional data
Multinominal logit model with time spent in SHG as explanatory variable
48 The Campbell Collaboration | www.campbellcollaboration.org
Study SHG Name Setting Additional Training Outcome Sample Data Collection
Study design Analysis
Husain et al., 2010 Swarna Jayanti Sahari Swarojgar Yojana
West Bengal, India
None (1) Economic Empowerment (2) Social Empowerment (Mobility)
Household data from unknown number of households from 6 municipalities
Survey Data Observational study with cross-sectional data
Logit model with time spent in SHG as explanatory variable
Kim et al., 2009 (incorporating Pronyk et al., 2006)
Intervention with Microfinance for AIDS and Gender Equity
South Africa Health Education (HIV prevention)
(1) Economic Empowerment (2) Social Empowerment (Mobility) (3) Psychological Empowerment (4) Intimate Partner Violence
1409 households in 12 villages
Survey Data + census Data
Cluster-randomized controlled trial with cross-sectional data
Cross-sectional comparison to determine risk ratio
Mahmud, 1994 BRAC, Grameen Bank, Bangladesh Rural Development Board, Women’s Entrepreneurship Development Program
Bangladesh Health Education (family planning)
(1) Social Empowerment (Family Size Decision Making)
806 households with currently married women in 8 villages
Survey Data Observational study with cross-sectional data
Logit model
Mukherjee & Kundu, 2012
Swarnajayanti Gram Swarojgar Yojona
West Bengal, India
None (1) Economic Empowerment (2) Social Empowerment
500 households in 14 villages
Survey Data Observational study with panel data
Multinominal logit model
49 The Campbell Collaboration | www.campbellcollaboration.org
Study SHG Name Setting Additional Training Outcome Sample Data Collection
Study design Analysis
Nessa et al., 2012 Grameen Bank, BRAC, and ASA
Bangladesh None (1) Economic Empowerment (2) Social Empowerment (Mobility) (3) Political Empowerment
600 households in 8 districts
Survey Data Observational study with cross-sectional data
OLS regression model with time spent in SHG as explanatory variable
Osmani, 2007 Grameen Bank Bangladesh None (1) Economic Empowerment
84 households in 4 villages
Survey Data Observational study with cross-sectional data
Instrumental variable regression analysis
Pitt et al., 2006 Grameen Bank Bangladesh None (1) Economic Empowerment (2) Social Empowerment (Mobility + Family Size Decision Making) (3) Political Empowerment
1798 households from 87 villages
Survey Data Observational study with panel data
Instrumental variable regression analysis
50 The Campbell Collaboration | www.campbellcollaboration.org
Study SHG Name Setting Additional Training Outcome Sample Data Collection
Study design Analysis
Rosenberg, 2011 Fondasyon Kole Zepol
Haiti Basic Education, Business skills, Rights Awareness, Health Education
(1) Social Empowerment (Family Size Decision Making)
192 households that selected in the survey
Survey Data Observational study with cross-sectional data
OLS regression model with dummy variable that is 1 if clients had been involved in the SHG for more than 12 months and 0 if less than 12 months
Sherman et al., 2010 Research Study John Hopkins School of Public Health
Chennai, India (South)
Health Education (HIV prevention), Business Skills
(1) Economic Empowerment (Sex Partners)
100 sex-workers in Chennai
Survey Data Cluster-randomized controlled trial with panel data
Difference-in-difference analysis
Steel et al., 1998 Save the Children & Association for Social Development
Bangladesh None (1) Social Empowerment (Family Size Decision Making)
6456 households in 15 villages
Survey Data Observational study with panel data
Difference-in-difference analysis
Swendeman et al., 2009
Sonagachi Project
West Bengal, India (Central)
Health Education (HIV prevention)
(1) Economic Empowerment (Sex Workers) (2) Social Empowerment (Sex Workers) (3) Psychological Empowerment (Sex Workers)
110 sex-workers in two towns
Survey Data Observational study with cross-sectional data
Cross-sectional comparison to determine odds ratio
51 The Campbell Collaboration | www.campbellcollaboration.org
4.2.2 Qualitative studies (review objective 2)
The 12 qualitative studies were also predominately from South Asia. Nine studies focused on SHGs in India. The remaining studies came from Nepal
(1), Bolivia (1) and Tanzania (1). Table 4.4 describes the included qualitative studies, including the name of the SHG, the setting, the sample, the data
collection, and the methods of analysis.
Most of the qualitative data were drawn from purposive or convenience samples of SHG participants through unstructured or semi-structured in-
depth interviews. Two studies (Dahal, 2014; Ramachandar & Pelto, 2009) randomly selected participants. Two other studies, (Maclean, 2012;
Mercer, 2002) used a case study methodology to describe how SHGs operate within a village context. Six studies (Dahal, 2014; Knowles, 2014; Kilby,
2011; Maclean, 2012; Mercer, 2002; Sahu & Singh, 2012) used focus groups in addition to individual interviews. Most of the studies did not name the
specific qualitative theory behind their analysis methodology but descriptions of their analysis process indicated that most studies used some
adaptation of grounded theory, content analysis or thematic analysis techniques.
We present further details in the qualitative synthesis below (Chapter 4.4).
Table 4.4: Summary data on qualitative studies
Author, Year Name or Description of SHG
SHG main activities Setting Sample Data Collection Analysis
Dahal 2014 Village Development Committees in Lamachaur
Microcredit, trainings and social awareness
Nepal Random Sample of 40 female SHG members, and 3 SHG leaders
Focus groups and in-depth interviews
Thematic Analysis
Kabeer 2011 BRAC, Nijera Kori, Saptagram and Samata
BRAC: Microcredit entrepreneurial skills, literacy; Nijera Kori, Saptagram and Samata: Savings, activism and collective awareness raising
Bangladesh Convenience selection of 31 women from 4 socially oriented SHGs
Loose life history approach, semi-structured interviews
Modified Grounded Theory
52 The Campbell Collaboration | www.campbellcollaboration.org
Author, Year Name or Description of SHG
SHG main activities Setting Sample Data Collection Analysis
Kilby 2011 77 small local groups in Karnataka and Pune
Microfinance; community based management of natural resources, sustainable agriculture, human rights
South India Women from 70 purposively selected NGO-initiated self-help groups
70 focus groups, 2 workshops and key informant interviews
Modified Grounded Theory using Ranking Exercise
Knowles 2014 Tamil Nadu Women's Association
Microfinance; community development South India Purposive selection of 196 female SHG members
In-depth semi-structured interviews, Structured Focus Groups, Participant Observation
Content Analysis
Kumari 2011 Gandhi Smaraka Grama Seva Kendram in Kerala
Microfinance South India Purposive sample from networked groups of women in one urban slum and one tribal area
Participant observation, informal chats, focus group discussions and interviews
Phenomenology
Maclean 2012 Credit with Rural Education in Luribay
Microfinance, Village banking training Bolivia Case study of one village banking program
28 in-depth interviews, 2 focus groups, and participation in 40 group meetings
Case Study
Mathrani 2006 Mahila Samakhya in Karnataka
Microsaving, literacy training, community development
South India Seven purposively-selected village SHGs
Unstructured interviews with participants
Modified Grounded Theory
Mercer 2002 Chagga village women's organizations in Hai District
Cooperative income-generation activities
Tanzania Case study of four village-based women's organizations
Group discussions, qualitative household interviews
Case Study
Pattenden 2011
Jagruthi Mahela Sanghathan in Karnataka
Income Diversification and Trainings (gender violence, discrimination, health, rights, agriculture)
South India All the members of three purposively selected, scheduled caste women’s associations in 3 villages
Two rounds of semi-structured interviews
Modified Grounded Theory
53 The Campbell Collaboration | www.campbellcollaboration.org
Author, Year Name or Description of SHG
SHG main activities Setting Sample Data Collection Analysis
Ramachandar 2009
Family Planning Association of India in Bellary
Microfinance and Family Planning, training (gender issues, credit management, leadership, income generating activities)
South India Random selection of 25 SHGs from 50 total groups within one organization
In-depth semi-structured interviews
Modified Grounded Theory
Sahu 2012 Madagadipet Self Help Groups
Microfinance South India Convenience sample of female SHG members from 6 different groups
6 Focus Group Discussions Content Analysis
54 The Campbell Collaboration | www.campbellcollaboration.org
4.3 CRITICAL APPRAISAL OF INCLUDED STUDIES
4.3.1 Risk of bias of quantitative studies
We relied on a risk of bias tool with 71 criteria that were related to selection bias and
confounding, performance bias, outcome and analysis reporting biases, and other
biases. The complete tool and a detailed assessment of the risk of bias of each
individual quantitative study can be found in Appendices 7.6 and 7.8.
Figure 4.2 shows that only three of the 23 quantitative studies were rated as having a
low risk of selection bias. Each of these studies was a cluster-randomized controlled
trial with a sufficient sample size to ensure equivalence in observable and
unobservable characteristics across the treatment and the control group. RCTs with
a small sample size were rated as having a medium risk of selection bias because the
studies usually did not show sufficient evidence that there was equivalence in
observable characteristics. In addition, quasi-experimental studies were usually not
convincing in their claims that selection bias was no longer an issue after controlling
for observable characteristics with statistical tools, such as propensity score
matching and multivariate regression analysis. We rated studies that used
propensity score matching with a large number of plausibly exogenous control
variables as having a medium risk of selection bias and studies that used
multivariate regression analysis as having a high risk of selection bias.
Of the 23 quantitative studies, five studies were rated as having a low risk of
performance bias. These studies usually had a control or comparison group that was
not in direct contact with the beneficiaries of the intervention to ensure the control
or comparison group was not contaminated by the intervention or the adoption of
practices by beneficiaries of the intervention as a result of their SHG membership.
Studies that included a comparison group that was in direct contact with the
beneficiaries but that took measures in their analysis or sampling strategy to
consider this were rated as having a medium risk of performance bias. For example,
Banerjee et al. (2015) acknowledged that the control group was contaminated by
other microfinance services similar to the intervention they evaluated. However, the
authors also demonstrated that the uptake of microcredit by beneficiaries was
significantly higher in the treatment villages. Hence, performance bias could be
rated as medium in this specific study. Other studies that included a comparison
group that was in close contact with the beneficiaries were rated as having a high
risk of performance bias.
Of the 23 included studies, six studies were rated as having a low risk of outcome
and analysis reporting bias. These studies did not show signs of inconsistent
reporting or unusual types of analyses. Several other studies were labeled as
medium risk of outcome and analysis reporting bias because of unclear explanation
of the outcome variables or the use of potentially flawed analyses. For example, we
rated studies that used potentially endogenous variables as explanatory variables as
55 The Campbell Collaboration | www.campbellcollaboration.org
having a medium risk of outcome and analysis reporting bias. In these cases the
outcome equations were potentially incorrectly specified. Finally, several studies did
only show tables for outcome variables that were significantly affected by self-help
groups and not for outcome variables that were not significantly affected. We labeled
these studies as having a high risk of outcome and analysis reporting bias because of
the potential for publication bias. We also labeled studies that used an explanatory
variable with the amount of time that respondents were members of SHGs as having
a high risk of outcome and analysis reporting bias. Such explanatory variables
increase the risk of bias due to a lack of accounting for potential nonlinearities in the
impact estimates of SHGs.
Finally, of the 23 included studies, eight were rated as having a low risk of other
bias. These studies did not show any other potential biases. But another 38 per cent
of the studies were rated as having a medium risk of potential bias, for example,
because studies did not explain well whether authors took measures to mitigate
concerns regarding the measurement of potentially sensitive outcome variables,
such as domestic violence. Studies with a high risk of other biases included studies
that relied extensively on recall data for outcome variables, which raised the
likelihood of social desirability bias. For example, SHG members may have had the
perception that enumerators would like to hear that SHG membership has resulted
in improvements in autonomy. Under such circumstances, the respondents might
have an incentive to underestimate their level of autonomy before the start of their
SHG membership and to overestimate their level of autonomy after the start of the
SHG membership.
There was almost complete agreement between the two reviewers in assessments of
the risk of selection and performance bias, but initially there were more
disagreements about the risk of outcome and analysis reporting biases and other
biases. In first instance, the reviewers disagreed about the risk of selection bias and
confounding for one of the 23 included studies (Desai & Tarozzi, 2011), risk of
performance bias for two of the 23 included studies (Holvoet, 2005; Sherman et al.,
2010), risk of outcome and analysis reporting bias for seven of the 23 included
studies (Ahmed, 2005; De Hoop et al., 2014; Sherman et al., 2010; Rosenberg et al.,
2011; Swendeman et al., 2009; Pitt et al., 2006; Nessa et al., 2012) and other biases
for 11 of the 23 included studies (Coleman, 2002; Banerjee et al., 2015; Desai and
Joshi, 2012; Garikipati, 2008; Garikipati, 2012; Kim et al., 2009; Pronyk et al.,
2006; Mukherjee and Kundu, 2012; Rosenberg et al., 2011; Osmani, 2007; Steele et
al., 1998). However, in all cases where there was no immediate agreement, the
reviewers reached agreement about the risk of bias assessment through consensus.
56 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.2: Risk of bias assessment of quantitative studies
4.3.2 Quality of qualitative studies
The appraisals of the qualitative studies are summarized in Figure 4.3 and
assessments by study are included in Appendix 9.5 The nine-question tool aimed to
determine whether a study was valid if the results were reported adequately and if
the findings would be helpful locally. The nine studies were considered valuable
based on responses to two screening questions and seven assessment questions.
There was almost complete agreement between the two researcher assessors. In two
cases associated with consideration of ethical issues and one case associated with the
relationship between the researcher and the participants, one researcher felt that
she could not tell whether a criterion was met, whereas the other researcher was able
to identify the information to answer the criteria (Pattenden, 2011; Ramachandar &
Pelto, 2009; Kumari, 2011).
5 Details of the quality appraisal assessment criteria are in Appendix 5.
38%
29%
24%
14%
38%
24%
10%
24%
24%
48%
67%
62%
0 % 20 % 40 % 60 % 80 % 100 %
Other biases
Outcome and analysis reporting bias
Performance bias
Selection bias
Low risk of bias
Medium risk of bias
High risk of bias
57 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.3: Summary of quality appraisal of qualitative studies
Figure 4.3 shows that there are important concerns regarding several of the quality
criteria for the included qualitative studies, although all included qualitative studies
had a clear statement of the study aims, appropriately used qualitative methodology,
had an appropriate research design, and reported clear statements of their findings.
Studies that received a “can’t tell” or “no” did so for several main reasons. With
respect to the recruitment strategy, authors did not always explain how the
participants were selected and why this selection could be considered the most
appropriate sampling strategy for the study. There was also not sufficient
explanation of the recruitment process such as who chose to participate and who
declined. With respect to data collection, authors did not adequately justify why they
had chosen one method over another. Few authors described their data collection
tools such as interview guides or their data format such as tape recordings or
handwritten notes. No author mentioned data saturation as a reason for stopping
recruitment. Most authors did not report information about the researcher-
participant relationship and did not examine the potential bias and influence they
introduced during all aspects of the study. In addition, very few authors described
whether and how ethical standards were maintained (such as informed consent).
The authors also did not discuss any ethical issues that the study raised. Finally,
many studies lacked an in-depth description of the data analysis process both in
terms of the methodology used and how the analysis was carried out.
0 % 20 % 40 % 60 % 80 % 100 %
Clear statement of study aims
Appropriate qualitative methodology
Appropriate research design
Appropriate recruitment strategy
Appropriate data collection method
Consideration of researcher relationship
Consideration of ethical issues
Rigorous data analysis
Clear statement of findings
100%
100%
100%
92%
82%
18%
64%
100%
8%
55%
72%
27%
0%
Yes Can't tell No
58 The Campbell Collaboration | www.campbellcollaboration.org
4.4 SYNTHESIS OF QUANTITATIVE STUDIES
This section presents results of meta-analysis of the effects of women’s self-help
groups on women’s economic, social, psychological, and political empowerment and
intimate partner violence (review objective 1). In addition to the preferred
specification for economic, social, psychological, and political empowerment and
intimate partner violence, we also present an extensive sensitivity analysis with
separate impact estimates for studies with high, medium, and low risk of bias, and
randomized controlled trials and quasi-experimental evaluations. Further, we
analyze heterogeneity by comparing effect sizes across geographic contexts, although
our sample size only permitted a narrative analysis of the differences across
geographic contexts. We also present a narrative analysis to determine the separate
effects of different components of self-help groups, such as microcredit,
microsavings, and training. Finally, we present a narrative analysis to determine
differences in effect sizes between studies within the same empowerment domain
that have different outcome measures and might thus measure different
empowerment constructs.
4.4.1 Economic Empowerment
Of the 23 included quantitative studies, ten included an impact estimate on women’s
economic empowerment that we were able to include in our meta-analysis, and eight
included an impact estimate on women’s economic empowerment but did not allow
for determining the effect size of the intervention. We summarize the measurement
of economic empowerment and the feasibility to include studies in the meta-analysis
in Table 4.5.
Table 4.5: Measurement of women’s economic empowerment
Study Definition of Variable Scale Included in Meta-Analysis?
Bali Swain & Wallentin (2009)
General index of women’s empowerment. Normalized score from 0-1
No; not able to estimate effect size
Banerjee et al. (2015)
Normalized index score that includes variables that measure the decision-making power of the female respondent in the household.
Normalized score from 0-1
Yes
Coleman (1999) Several variables that emphasize the female ownership of assets.
Several binary variables
No; not able to estimate effect size
De Hoop et al., (2014)
Dummy variable that is 1 for women who make decisions about food expenditures.
Binary Yes
Deininger and Liu (2013)
Dummy variable that is 1 for women who are able to save individually.
Binary Yes; after imputing the standard deviation
59 The Campbell Collaboration | www.campbellcollaboration.org
Desai and Joshi (2012)
Several dummy variables associated with women’s decision-making power about schooling and health expenditures.
Several binary variables
Yes
Garikipati (2008) & Garikipati (2012)
Women’s labor supply. Continuous variable
No; not able to estimate effect size
Holvoet (2005) Several dummy variables associated with women’s decision-making power in economic and non-economic domains.
Several binary variables
No; not able to estimate effect size
Husain et al. (2010)
Several variables associated with women’s decision-making power in the economic domain, which are 0 if the woman has no decision-making power, 0.5 if there is joint decision-making and 1 if the woman is the sole decision-maker.
Aggregate score of categorical variables
No; not able to estimate effect size
Kim et al. (2009) & Pronyk et al. (2006)
Dummy variable that is 1 if the woman believes her contribution to the household is positive
Binary variable Yes
Mukherjee & Kundu (2012)
Several dummy variables associated with women’s decision-making power about household expenditures.
Index of binary variables
No; not able to estimate effect size
Nessa et al. (2012)
Categorical variable associated with the economic decision-making power of the woman in the household.
Binary variable Yes
Osmani (2007) Categorical variable that measures the perception of the woman on how well she would be able to take care of herself.
Ordered categorical variable
Yes
Pitt et al. (2006) Several dummy variables that are associated with women’s decision-making power about household expenditures, access to funds, and borrowing money.
Index of binary variables
Yes
Sherman et al. (2010)
Self-reported number of sex-exchange partners. Continuous variable
Yes
Steel et al. (1998)
Several dummy variables associated with women’s decision-making power with respect to medical expenditures, borrowing, and housing repairs.
Several binary variables
No; not able to estimate effect size
Swendeman et al. (2009)
Dummy variables related to decision-making power of female sex workers.
Several binary variables
Yes
The table demonstrates that women’s empowerment was measured in different ways
across studies. However, with a few exceptions, women’s economic empowerment
was reflected in women’s bargaining power or decision-making power. We were not
able to include the few studies that do not measure women’s bargaining power but
another component of women’s economic empowerment in the meta-analysis
because we were not able to calculate effect sizes for these specific studies. We
discuss the results of these studies in a narrative synthesis. The measurement of
women’s bargaining power was mostly associated with decisions about expenditures
and borrowing, but for the specific case of sex workers bargaining power was also
associated with decision-making power about the number of clients for the sex
worker.
60 The Campbell Collaboration | www.campbellcollaboration.org
The measurement of women’s bargaining power might thus measure a different
construct for sex workers. Therefore, we conducted meta-analyses with and without
studies that measure women’s bargaining power for sex workers. In addition, we
also conducted a meta-analysis without the study of Deininger and Liu (2013) who
emphasize women’s ability to save individually. Although this concept might be
related to women’s bargaining power, women’s ability to save individually could also
be considered a different construct.
Figure 4.4 presents the forest plot with the results of the meta-analysis of
randomized controlled trials. From the analysis, it appears that women’s self-help
groups have an average positive effect of 0.22 standard deviations on women’s
economic empowerment (SMD=0.22, 95% confidence interval (CI)=-0.01, 0.44;
evidence from 4 studies), but one which is not statistically significant at the 95 per
cent level. The meta-analysis also suggests strong heterogeneity in the impact
estimates of women’s self-help groups on women’s economic empowerment.
Observed heterogeneity in effect sizes ranged from 0.01 to 0.45 standard deviations
and statistical tests suggest there is support that this heterogeneity is real rather
than due to random sampling error (Q=16, Tau-sq=0.04, I-sq=81%). However, we
are not able to interpret I-squared as an absolute indicator of heterogeneity
(Borenstein et al., 2009; Higgins, 2011), and the estimate of the variance component
tau-squared is low, suggesting the level of between-study heterogeneity may be
limited. We should be careful in interpreting these results, however, as these tests
are not always appropriate for a small number of studies (ibid.).
61 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.4: The effects of women’s self-help groups on women’s economic
empowerment (randomized controlled trials)
There are various differences in the implementation, context and risk of bias of the
RCTs. First, there was heterogeneity in the types of self-help groups that were
evaluated using randomized controlled trials. For example, the study by Banerjee et
al. (2015) focused on a self-help group intervention without a training component.
And the study of Sherman et al. (2010) assessed the impact of a women’s self-help
group program on the economic empowerment of female sex workers. Arguably, the
included studies were not fully comparable to each other and this needed to be taken
into consideration in a sensitivity analysis. We illustrate this by a meta-regression that
demonstrated that the estimated effect sizes on economic empowerment of RCTs of
interventions with a training component were substantively and statistically
significantly higher (SMD=0.31, 95% CI=-0.16, 0.45; Q=0.6, Tau-sq=0.00, I-sq=0%;
evidence from 3 studies) than the effect size of the study by Banerjee et al. (2015). The
changes in the confidence interval and the reductions in the indicators to measure
heterogeneity after excluding the studies without a training component also suggest
that SHG programs with training have substantively higher effect sizes on women’s
bargaining power than SHG programs without a training component and that
heterogeneity is mostly caused by including studies with a training component. The
effect size of the study of Sherman et al. (2010) with an emphasis on sex workers is
also not substantively different from the effect sizes of other interventions with a
training component. Thus, excluding the study of Sherman et al. (2010), which might
potentially measure a different empowerment construct, does not change the
interpretation of the results. The average effect size of SHGs on women’s economic
NOTE: Weights are from random effects analysis
Overall (I-squared = 81.3%, p = 0.001)
Banerjee et al., 2014 India
Kim et al., 2009 + Pronyk et al., 2006, South Africa
Sherman et al., 2010, India
Study
ID
Desai and Joshi, 2012, India
0.22 (-0.01, 0.44)
0.01 (-0.04, 0.05)
0.45 (0.06, 0.84)
0.30 (-0.11, 0.70)
ES (95% CI)
0.28 (0.12, 0.45)
100.00
35.51
17.48
16.82
%
Weight
30.19
0.22 (-0.01, 0.44)
0.01 (-0.04, 0.05)
0.45 (0.06, 0.84)
0.30 (-0.11, 0.70)
ES (95% CI)
0.28 (0.12, 0.45)
100.00
35.51
17.48
16.82
%
Weight
30.19
Impact SHGs on Economic Empowerment Based on RCTs
0-.842 0 .842
62 The Campbell Collaboration | www.campbellcollaboration.org
empowerment is 0.20 SMD when we exclude studies with an emphasis on sex workers
(SMD=0.20, 95% CI=-0.05, 0.46; Q=14, Tau-sq=0.04, I-sq=86% ; evidence from 3
studies) and 0.31 SMD when we exclude studies with an emphasis on sex workers and
studies without a training component (SMD=0.31, 95% CI =0.16, 0.46; Q=0.62, Tau-
sq=0.00, I-sq=0% ; evidence from 2 studies).
The effect sizes of each of the studies included in Figure 4.4 were all potentially subject
to various biases despite the random allocation of the intervention. Both the study of
Sherman et al. (2010) and Kim et al. (2009) were rated as having a medium risk of
selection bias due to the small sample size of these studies. Furthermore, the study of
Banerjee et al. (2015) was rated as having a medium risk of performance bias because
of contamination of the control group by various other microfinance initiatives. In
addition, the study of Sherman et al. (2010) was rated as having a high risk of
performance bias because the control group lives in the same locality as the
beneficiaries of the intervention, which may result in spillovers. Meta-regressions did
not suggest statistically significant differences in effect sizes between RCTs that were
rated as having differential risks of bias. Nonetheless, the evidence for heterogeneity
suggested that we were not able to derive strong conclusions about the effects of
women’s self-help groups on women’s economic empowerment based on these
studies alone. Furthermore, statistical heterogeneity in the estimates suggested that
it might be beneficial to include additional studies with a higher degree of precision.
We conducted a separate meta-analysis of the effects of women’s self-help groups on
women’s economic empowerment based on quasi-experimental evaluations (Figure
4.5). From the analysis, it appears that women’s self-help groups have a positive effect
on women’s economic empowerment, which is statistically significant at the 95 per
cent level (SMD=0.32, 95% CI=0.14, 0.50; evidence from 6 studies). Again, the meta-
analysis suggested strong heterogeneity. Effect sizes of the studies ranged between
0.03 and 1.15 standard deviations, while statistical heterogeneity tests suggested that
a substantial percentage of the observed heterogeneity in the effect size is real rather
than random sampling error (Q=29, I-sq=83%), albeit with a small estimated
variance component (tau-sq=0.03).
63 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.5: The effects of women’s self-help groups on women’s economic
empowerment (quasi-experimental evaluations)
Additional analysis suggested that the heterogeneity in the impact estimates could
be partly explained by the inclusion of quasi-experimental studies with a high risk of
selection-bias. Meta-analyses of quasi-experimental studies with a medium and high
risk of selection-bias indicated that the impact estimate of studies with a high risk of
selection-bias is notably higher than the impact estimate of studies with a medium
risk of selection-bias (Figure 11.1 and 11.2 in Appendix 11). The meta-analyses
indicated that the impact estimate of quasi-experimental studies with a high risk of
selection bias was on average 0.65 standard deviations (SMD=0.65, 95% CI=0.33,
0.98; Q=29, Tau-sq=0.04, I-sq=42%; evidence from 3 studies), which is
approximately three times as high as the effect size for RCTs (0.22 standard
deviations). The average impact estimate of studies with a medium risk of selection
bias of 0.17 standard deviations (SMD=0.17, 95% CI=0.03, 0.34; Q=9, Tau-sq=0.01,
I-sq=78%; evidence from 3 studies) is much closer to the impact estimate of
randomized controlled trials. These results therefore suggested that we could pool
RCTs and quasi-experimental studies with a medium risk of selection-bias.
Meta-regressions presented further evidence for the inability to pool randomized
controlled trials and quasi-experimental studies with a high risk of selection-bias.
The estimated effect sizes on economic empowerment of RCTs were substantively
and statistically significantly lower than the effect sizes of quasi-experimental
studies with a high risk of selection-bias (β=-0.44; 95% CI=-0.81, -0.07). At the
same time, meta-regression indicated that the estimated effect sizes on economic
empowerment of RCTs were not statistically significantly different from the effect
sizes of quasi-experimental studies with a medium risk of selection-bias (β=-0.04;
NOTE: Weights are from random effects analysis
Overall (I-squared = 82.7%, p = 0.000)
Swendeman et al., 2009, India
ID
De Hoop et al., 2014 India
Pitt et al., 2006, Bangladesh
Deininger and Liu, 2013 India
Osmani, 2007, Bangladesh
Nessa et al., 2012, Bangladesh
Study
0.32 (0.14, 0.50)
1.15 (0.47, 1.83)
ES (95% CI)
0.03 (-0.21, 0.27)
0.12 (0.03, 0.21)
0.28 (0.20, 0.36)
0.37 (-0.10, 0.83)
0.65 (0.41, 0.89)
100.00
5.51
Weight
17.90
24.43
24.89
9.46
17.82
%
0.32 (0.14, 0.50)
1.15 (0.47, 1.83)
ES (95% CI)
0.03 (-0.21, 0.27)
0.12 (0.03, 0.21)
0.28 (0.20, 0.36)
0.37 (-0.10, 0.83)
0.65 (0.41, 0.89)
100.00
5.51
Weight
17.90
24.43
24.89
9.46
17.82
%
Impact SHGs on Economic Empowerment Based on Quasi-Experimental Studies 0-1.83 0 1.83
64 The Campbell Collaboration | www.campbellcollaboration.org
95% CI=-0.09, 0.29). Based on these meta-analyses and meta-regressions we
decided to only pool randomized controlled trials and quasi-experimental
evaluations with a medium risk of selection-bias.
Further analyses of the quasi-experimental studies with a medium risk of selection-
bias did not suggest evidence for differences in estimated effect sizes between
evaluated self-help groups with and without a training component. A meta-
regression indicated that the estimated effect sizes on economic empowerment of
quasi-experimental studies with a medium risk of selection-bias for SHGs with a
training component are 0.06 SD higher than quasi-experimental studies with a
medium risk of selection-bias focusing on SHGs without a training component. The
results were, however, not statistically distinguishable from each other at the 5 per
cent significance level (β=0.06; 95% CI=-0.33, 0.45).
The meta-analysis of quasi-experimental evaluations with a medium risk of
selection-bias also indicated that studies with a high risk of spillovers might
underestimate the impact of women’s self-help groups on women’s economic
empowerment possibly because of contamination of the comparison group. A meta-
regression indicated that the estimated effect size of quasi-experimental studies with
a medium risk of selection-bias and a high risk of performance bias is statistically
and significantly lower than the estimated effect size of quasi-experimental studies
with a medium risk of selection-bias and a low or medium risk of performance bias
(β=-0.17; 95% CI=-0.06, -0.28). We explore this relationship further in the pooled
analysis of randomized controlled trials and quasi-experimental studies with a
medium risk of selection-bias.
Finally, we conducted meta-analysis of randomized controlled trials and quasi-
experimental evaluations with a medium risk of selection-bias to determine the
pooled effects of women’s self-help groups on women’s economic empowerment
(Figure 4.6). The analysis suggests that women’s self-help groups have a positive
effect of 0.18 standard deviations on women’s economic empowerment. The effect is
statistically significant at the 95 per cent level (SMD=0.18, 95% CI=0.05, 0.31;
evidence from 7 studies). The analysis also indicated strong statistical heterogeneity
in the impact estimates (Q=46, Tau-sq=0.02, I-sq=87%) with effect sizes ranging
from 0.01 to 0.45 standard deviations.
65 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.6: Effects of women’s self-help groups on women’s economic
empowerment (RCTs and quasi-experimental evaluations with a medium risk of
selection-bias)
Additional analyses suggested that the heterogeneity in the effect sizes were partly
explained by training. Analysis of the effects of women’s self-help groups on
women’s economic empowerment excluding interventions without a training
component (Figure 11.3 in Appendix 11), suggests groups with a training component
have a statistically significant positive effect of 0.26 standard deviations on women’s
economic empowerment (SMD=0.26, 95% CI=0.17, 0.35; Q=5, Tau-sq=0.00, I-
sq=17%; evidence from 5 studies). Furthermore, meta-regression suggested that the
effect size of studies with a low or medium risk of selection bias that focused on
interventions with a training component had a statistically significantly larger effect
size than studies with a low or medium risk of selection bias that focused on
interventions without a training component (β=0.20; 95% CI=0.06, 0.34). In
contrast, the evidence for positive effects on economic empowerment of SHGs
without a training component is rather less convincing: the average effect size
estimated was only 0.06 SMD and was not statistically significant at the 95 per cent
significance level (SMD=0.06, 95% CI=-0.05, 0.16; Q=5, Tau-sq=0.00, I-sq=78%;
evidence from 2 studies) (Figure 11.4 in Appendix 11). However, it is hard to
interpret this finding, because the quantitative studies provide only very limited
information about the contents of the training included in the evaluated SHGs.
NOTE: Weights are from random effects analysis
Overall (I-squared = 86.8%, p = 0.000)
Banerjee et al., 2014 India
Sherman et al., 2010, India
Study
De Hoop et al., 2014 India
Pitt et al., 2006, Bangladesh
ID
Desai and Joshi, 2012, India
Deininger and Liu, 2013 India
Kim et al., 2009 + Pronyk et al., 2006, South Africa
0.18 (0.05, 0.31)
0.01 (-0.04, 0.05)
0.30 (-0.11, 0.70)
0.03 (-0.21, 0.27)
0.12 (0.03, 0.21)
ES (95% CI)
0.28 (0.12, 0.45)
0.28 (0.20, 0.36)
0.45 (0.06, 0.84)
100.00
20.32
6.80
%
12.15
18.81
Weight
15.45
19.34
7.14
0.18 (0.05, 0.31)
0.01 (-0.04, 0.05)
0.30 (-0.11, 0.70)
0.03 (-0.21, 0.27)
0.12 (0.03, 0.21)
ES (95% CI)
0.28 (0.12, 0.45)
0.28 (0.20, 0.36)
0.45 (0.06, 0.84)
100.00
20.32
6.80
%
12.15
18.81
Weight
15.45
19.34
7.14
Impact SHGs on Economic Empowerment Based on RCTs and Medium Risk of Bias Quasi-Experimental Studies
0-.842 0 .842
66 The Campbell Collaboration | www.campbellcollaboration.org
Table 4.6 summarizes the results of all meta-analyses with an emphasis on economic
empowerment. Interestingly, the study by Sherman et al. (2010), which studied the
bargaining power of sex workers towards clients, did not show an effect size that was
either substantively or statistically significantly different from the effect sizes of the
other studies. The interpretation of our results thus did not change when we
excluded this study. Similarly, our results did not change substantively when we
excluded the study of Deininger and Liu (2013), which focuses on women’s ability to
save individually.
We did not find evidence for differences in effect sizes of studies with a low or
medium risk of spillovers and studies with a high risk of spillovers in the pooled
sample. Our analyses also did not suggest evidence for significant differences in
effect sizes between studies with low, medium, and high outcome and analysis
reporting and other biases, respectively.
Table 4.6: Summary of effects of SHGs on economic empowerment
Description Effect Size Confidence Interval
Randomized controlled trials 0.22 SMD -0.01 SMD, 0.44 SMD
Quasi-experimental studies 0.32 SMD 0.14 SMD, 0.50 SMD
RCTs and quasi-experimental studies with medium risk of selection bias
0.18 SMD 0.05 SMD, 0.31 SMD
Quasi-experimental studies with high risk of selection bias 0.65 SMD 0.33 SMD, 0.98 SMD
RCTs and quasi-experimental studies with medium risk of selection bias with an emphasis of SHGs that include training
0.26 SMD 0.17 SMD, 0.35 SMD
RCTs and quasi-experimental studies with medium risk of selection bias with an emphasis of SHGs that do not include training
0.06 SMD -0.05 SMD, 0.16 SMD
A number of quasi-experimental studies could not be included in the meta-analysis.
However, excluding these studies would not have significantly, either substantively
or statistically, changed the results from the meta-analysis. Either the results of
these studies were not very different from the results of the meta-analysis or the risk
of bias of the study would have been too high to be included in the preferred
specification for the meta-analysis. Coleman (2002) found positive but small effects
of women’s self-help groups in Thailand on women’s economic empowerment.
These results could not be included in the meta-analysis because Coleman (2002)
focused on the effects of time in self-help groups rather than the effects of
participation in self-help groups. In addition, the study did not focus on women’s
bargaining power but on women’s ownership of assets, such as land, so the outcome
indicators were not considered comparable to other studies. Garikipati (2008, 2012)
assessed the impact of women’s self-help groups on different components of
women’s empowerment, including women’s bargaining power as well as other
components of women’s economic empowerment, but found no evidence of positive
67 The Campbell Collaboration | www.campbellcollaboration.org
effects. These studies were not included because Garikipati (2008, 2012) used the
time in self-help groups rather than the participation in self-help groups as an
explanatory variable. Holvoet (2005) found that self-help groups had bigger positive
effects on women’s economic empowerment when self-help groups provided
training in addition to financial services. However, although the study focused on
women’s bargaining power, the study was considered high risk of selection bias.
Husain et al. (2010) suggested positive effects of women’s self-help groups on
economic empowerment, including women’s bargaining power, but did not present
the point estimates regarding the impact of women’s self-help groups, and the study
was considered high risk of selection-bias. Finally, Mukherjee and Kundu (2012)
also suggested a positive effect of women’s self-help groups on women’s economic
empowerment, again including women’s bargaining power. However, they did not
present the quantitative point estimates, and the study was considered high risk of
selection-bias.
4.4.2 Social Empowerment
We also synthesized the effects of women’s self-help groups on women’s social
empowerment using meta-analysis. Of the 23 included quantitative studies, ten
included an impact estimate that we were able to include in meta-analysis, and five
included an impact estimate for women’s social empowerment but did not allow
determination of the effect size of the intervention (Table 4.7). Analysis of outcomes
indicated that social empowerment relates to two types of outcome variables: 1)
outcome variables that are associated with women’s mobility; and 2) outcome
variables that relate to reproductive behavior and the bargaining power of women
over family-size decision-making. We therefore conducted both pooled and stratified
meta-analyses of studies according to these constructs.
Table 4.7: Measurement of women’s social empowerment
Study Definition of Variable Scale Included in Meta-Analysis?
Bali Swain & Wallentin (2009)
General index of women’s empowerment. Normalized score from 0-1
No; not able to estimate effect size
De Hoop et al., (2014)
Several dummy variables that measure women’s autonomy to go out without their husband’s permission
Several binary variables
Yes
Deininger and Liu (2013)
Several dummy variables that measure women’s autonomy to go out without their husband’s permission
Several binary variables
Yes; after imputing the standard deviation
Desai and Tarozzi (2011)
Several dummy variables that measure women’s decision-making power about family-size decision-making
Several binary variables
Yes; after calculating pooled effect size
Desai and Joshi (2012)
Dummy variable associated with family-size decision-making
Binary variable Yes
68 The Campbell Collaboration | www.campbellcollaboration.org
Husain et al. (2010)
Several variables associated with women’s mobility, which are 0 if the woman has no decision-making power, 0.5 if there is joint decision-making and 1 if the woman is the sole decision-maker.
Aggregate score of categorical variables
No; not able to estimate effect size
Kim et al. (2009) & Pronyk et al. (2006)
Dummy variable that is 1 if the woman challenges gender norms
Binary variable Yes
Mahmud (1994) Dummy variable that is 1 if the woman is sterilized Binary variable Yes
Nessa et al. (2012)
Categorical variable associated with freedom of movement of woman
Categorical variable
Yes
Pitt et al. (2006) Several dummy variables that are associated with women’s mobility and women’s bargaining power over family-size decision-making
Index of binary variables
Yes
Rosenberg et al. (2011)
Several dummy variables that are associated with reproductive behavior and family-size decision-making
Several binary variables
No
Steel et al. (1998) Dummy variable that is 1 when the woman uses contraceptives
Binary variable Yes
Swendeman et al. (2009)
Several dummy variables that are associated with reproductive behavior
Several binary variables
Yes
Our meta-analysis commenced with the synthesis of results from randomized
controlled trials. The meta-analysis was based on three studies, two of which showed
close to identical point estimates. However, the analysis also indicated strong
heterogeneity in the impact estimates (Figure 4.7). The effect sizes ranged from -
0.23 to 0.45 standard deviations, and the pooled effect size was not statistically
significantly different from zero (SMD=0.31, 95% CI=-0.09, 0.70; Q=3, Tau-
sq=0.06, I-sq=38%; evidence from 3 studies).
There were also potentially important differences between the three studies included
in the meta-analysis. Two of the studies focused on family-size decision-making
(Desai & Tarozzi, 2011; Desai & Joshi, 2012), while the study of Kim et al. (2009)
presents the impact of a self-help group on an outcome variable associated with the
challenging of gender norms by the women respondents. Unfortunately, the latter
outcome variable was not very well explained in the paper, but we interpret it as
being associated with women’s family-size decision-making because the intervention
mostly focused on that aspect of women’s social empowerment. Second, each of the
studies took place in a different part of the world. The study of Desai and Tarozzi
(2011) focused on Ethiopia, while the study of Kim et al. (2009) presented impact
estimates in the setting of South Africa. Finally, Desai and Joshi (2012) focused on
the impact of women’s self-help groups on women’s social empowerment in the
context of India. Third, although the studies of Desai and Joshi (2012) and Kim et al.
(2009) both include a training component, the study of Desai and Tarozzi (2012)
focused on a self-help group intervention without a training component. Fourth,
there were differences in the risk of bias assessment across the three studies. Clearly,
the sheer number of differences between the three different studies made it
impossible to explain the differences in the effect sizes across the three studies based
69 The Campbell Collaboration | www.campbellcollaboration.org
on a quantitative analysis alone. Therefore, we refrained from undertaking a meta-
regression to examine the differences in the effect sizes.
Figure 4.7: The effect of women’s self-help groups on women’s social empowerment
(randomized controlled trials)
We interpreted the findings of the RCTs as evidence for positive effects of SHGs on
women’s family-size decision-making power and not of evidence for positive effects
on women’s mobility. None of the RCTs focused on women’s mobility. In later stages
of our analysis we found evidence that women’s family-size decision-making power
and women’s mobility should not be considered part of the same construct.
We also conducted meta-analysis of quasi-experimental evaluations examining the
effects of women’s self-help groups on women’s social empowerment (Figure 4.8).
The analysis suggested that self-help groups have a positive effect on women’s social
empowerment. The point estimate of 0.19 standard deviations is significant at the 95
per cent significance level (SMD=0.19, 95% CI=0.09, 0.29; evidence from 7 studies).
The results also indicated significant statistical heterogeneity (Q=3, Tau-sq=0.01, I-
sq=48%) and the effect size ranged from 0.04 to 0.88 standard deviations across
studies.
NOTE: Weights are from random effects analysis
Overall (I-squared = 37.6%, p = 0.202)
ID
Study
Desai and Joshi, 2012, India
Desai and Tarozzi, 2013, Ethiopia
Kim et al., 2009 + Pronyk et al., 2006, South Africa
0.31 (-0.09, 0.70)
ES (95% CI)
0.45 (0.29, 0.62)
-0.23 (-0.96, 0.50)
0.44 (-0.51, 1.39)
100.00
Weight
%
64.92
21.00
14.08
0.31 (-0.09, 0.70)
ES (95% CI)
0.45 (0.29, 0.62)
-0.23 (-0.96, 0.50)
0.44 (-0.51, 1.39)
100.00
Weight
%
64.92
21.00
14.08
Impact SHGs on Social Empowerment Based on RCTs
0-1.39 0 1.39
70 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.8: Effects of women’s self-help groups on women’s social empowerment
(quasi-experimental evaluations)
Our analyses suggested that part of the heterogeneity in the effect sizes can be
explained by differences in the risk of selection-bias across quasi-experimental
studies. We found strong differences between the effect sizes of studies with a high
and medium risk of selection-bias, respectively (Figure 11.5 and 11.6 in Appendix 11).
The average effect size for quasi-experimental studies with a high risk of selection-
bias was estimated at 0.37 standard deviations (SMD=0.37, 95% CI=0.18, 0.56;
Q=3, Tau-sq=0.01, I-sq=10%; evidence from 4 studies), and statistically significantly
different from the average of 0.13 standard deviations for quasi-experimental
studies with a medium risk of selection bias (SMD=0.13, 95% CI=0.07, 0.19; Q=1,
Tau-sq=0.00, I-sq=0%; evidence from 3 studies). Our analysis thus suggested
studies with a high risk of selection-bias were biased and should not be pooled with
studies with a medium risk of selection-bias. Meta-regression confirmed that the
estimated effect size for quasi-experimental studies with a high risk of selection-bias
was significantly higher than the estimated effect size on social empowerment of
quasi-experimental studies with a medium risk of selection-bias (β=0.22, 95%
CI=0.06, 0.39). Based on these analyses we concluded that, while we could pool
quasi-experimental studies with a medium risk of selection-bias with randomized
controlled trials, we should not pool these studies alongside quasi-experimental
studies with a high risk of selection-bias.
We also estimated stratified meta-analyses for quasi-experimental studies focusing
on women’s family-size decision-making and women’s mobility, respectively. We
found large and positive pooled effects of studies with a high risk of selection bias on
women’s family-size decision-making (SMD=0.53, 95% CI=0.22, 0.85; Q=18, Tau-
NOTE: Weights are from random effects analysis
Overall (I-squared = 47.5%, p = 0.076)
Swendeman et al., 2009, India
Study
Steel et al., 1998, Bangladesh
Nessa et al., 2012, Bangladesh
Pitt et al., 2006, Bangladesh
Deininger and Liu, 2013 India
Rosenberg et al., 2011, Haiti
ID
De Hoop et al., 2014 India
0.19 (0.09, 0.29)
0.88 (-0.89, 2.65)
0.32 (0.16, 0.49)
0.79 (0.26, 1.32)
0.12 (0.03, 0.22)
0.15 (0.07, 0.22)
0.22 (-0.24, 0.69)
ES (95% CI)
0.04 (-0.20, 0.27)
100.00
0.31
%
18.52
3.21
29.67
31.96
4.15
Weight
12.18
0.19 (0.09, 0.29)
0.88 (-0.89, 2.65)
0.32 (0.16, 0.49)
0.79 (0.26, 1.32)
0.12 (0.03, 0.22)
0.15 (0.07, 0.22)
0.22 (-0.24, 0.69)
ES (95% CI)
0.04 (-0.20, 0.27)
100.00
0.31
%
18.52
3.21
29.67
31.96
4.15
Weight
12.18
Impact Self-Help Groups on Social Empowerment Based on Quasi-Experimental Evaluations
0-2.65 0 2.65
71 The Campbell Collaboration | www.campbellcollaboration.org
sq=0.08, I-sq=83%; evidence from 4 studies) (Figure 11.7 in Appendix 11). However,
the results are likely to be biased because the estimates are significantly larger than
the impact estimates of Pitt et al. (2006), the only quasi-experimental study with a
medium risk of selection bias that focuses on family-size decision-making
(SMD=0.06, 95% CI=-0.04, 0.15). Analysis of studies of effects on women’s
mobility, of which only quasi-experimental studies with a medium risk of selection
bias were available, suggested positive and statistically significant effects
(SMD=0.18, 95% CI=0.06, 0.31; Q=7, Tau-sq=0.01, I-sq=71%; evidence from 3
studies) (Figure 4.9).
Figure 4.9: Effects of women’s self-help groups on women’s mobility (quasi-
experimental evaluations)
Finally, we estimated the pooled effects of self-help groups on social empowerment
across randomized controlled trials and quasi-experimental studies with a medium
risk of selection-bias. The analysis suggested an average positive and statistically
significant effect of 0.18 standard deviations (SMD=0.18, 95% CI=0.06, 0.31; Q=15,
Tau-sq=0.01, I-sq=67%; evidence from 6 studies) (Figure 4.10).
NOTE: Weights are from random effects analysis
Overall (I-squared = 70.8%, p = 0.033)
Pitt et al., 2006, Bangladesh
Study
Deininger and Liu, 2013 India
De Hoop et al., 2014 India
ID
0.18 (0.06, 0.31)
0.29 (0.19, 0.38)
0.15 (0.07, 0.22)
0.04 (-0.20, 0.27)
ES (95% CI)
100.00
39.81
%
42.45
17.74
Weight
0.18 (0.06, 0.31)
0.29 (0.19, 0.38)
0.15 (0.07, 0.22)
0.04 (-0.20, 0.27)
ES (95% CI)
100.00
39.81
%
42.45
17.74
Weight
Impact SHGs on Mobility Based on Quasi-Experimental Studies 0-.378 0 .378
72 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.10: Effects of women’s self-help groups on women’s social empowerment
(RCTs and quasi-experimental evaluations with a medium risk of selection-bias)
Interestingly, all RCTs included in the meta-analysis focused on women’s bargaining
power over family-size decision-making. Almost all quasi-experimental studies
focused only on women’s mobility. Only the study of Pitt et al. (2006) presented a
weighted average estimate for women’s social mobility and family-size decision-
making. The difference in emphasis between RCTs and quasi-experimental studies
might explain why randomized controlled trials tend to show a larger effect on social
empowerment than quasi-experimental studies with a medium risk of selection-bias.
Meta-analysis results also indicated that SHGs have a stronger effect on women’s
family-size decision-making power than on women’s mobility (Figures 4.11 and
4.12). The average effect of SHGs on women’s family-size decision-making power
appears to be 0.26 standard deviations (SMD=0.26, 95% CI=-0.04, 0.56; Q=21,
Tau-sq=0.07, I-sq=86% ; evidence from 4 studies), while the average effect on
women’s mobility appears to be 0.18 standard deviations (SMD=0.18, 95% CI=0.06,
0.31; Q=7, Tau-sq=0.01, I-sq=71% ; evidence from 3 studies).
Thus, we interpret this finding as suggesting that SHGs have a larger impact
estimate on family-size decision-making than on women’s mobility. The larger effect
on family-size decision-making was also illustrated by one study which assessed
within-study impacts on both women’s family-size decision-making power and
NOTE: Weights are from random effects analysis
Overall (I-squared = 66.8%, p = 0.010)
Deininger and Liu, 2013 India
Pitt et al., 2006, Bangladesh
Desai and Joshi, 2012, India
Kim et al., 2009 + Pronyk et al., 2006, South Africa
Study
ID
Desai and Tarozzi, 2013, Ethiopia
De Hoop et al., 2014 India
0.18 (0.06, 0.31)
0.15 (0.07, 0.22)
0.12 (0.03, 0.22)
0.45 (0.29, 0.62)
0.44 (-0.51, 1.39)
ES (95% CI)
-0.23 (-0.96, 0.50)
0.04 (-0.20, 0.27)
100.00
29.98
28.70
21.41
1.70
%
Weight
2.77
15.45
0.18 (0.06, 0.31)
0.15 (0.07, 0.22)
0.12 (0.03, 0.22)
0.45 (0.29, 0.62)
0.44 (-0.51, 1.39)
ES (95% CI)
-0.23 (-0.96, 0.50)
0.04 (-0.20, 0.27)
100.00
29.98
28.70
21.41
1.70
%
Weight
2.77
15.45
Impact SHGs on Social Empowerment RCTs and Medium Risk of Bias Quasi-Experimental Studies
0-1.39 0 1.39
73 The Campbell Collaboration | www.campbellcollaboration.org
women’s mobility, finding positive effects on women’s family-size decision-making
but no evidence for positive effects on women’s mobility (Pitt et al., 2006).
Figure 4.11: Effects of women’s self-help groups on women’s mobility
NOTE: Weights are from random effects analysis
Overall (I-squared = 70.8%, p = 0.033)
Pitt et al., 2006, Bangladesh
ID
Deininger and Liu, 2013 India
Study
De Hoop et al., 2014 India
0.18 (0.06, 0.31)
0.29 (0.19, 0.38)
ES (95% CI)
0.15 (0.07, 0.22)
0.04 (-0.20, 0.27)
100.00
39.81
Weight
42.45
%
17.74
0.18 (0.06, 0.31)
0.29 (0.19, 0.38)
ES (95% CI)
0.15 (0.07, 0.22)
0.04 (-0.20, 0.27)
100.00
39.81
Weight
42.45
%
17.74
Impact SHGs on Mobility Based on RCTs and Medium Risk of Bias Quasi-Experimental Studies 0-.378 0 .378
74 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.12: Effects of women’s self-help groups on women’s family-size decision-
making
Our analyses also suggested that training in SHGs might have stronger effects on
women’s family-size decision-making power than on women’s mobility. For family-
size decision-making we found that the effect sizes of studies that focus on SHGs
with a training element were substantially and statistically significantly higher than
the effect sizes of studies without a training element (β=0.38; 95% CI=0.19, 0.57).
Additional meta-analyses suggested that the effect size of SHGs on family-size
decision-making was positive and statistically significant at the 95 per cent
significance level when we excluded studies without a training component
(SMD=0.41, 95% CI=0.19, 0.63; Q=3, Tau-sq=0.02, I-sq=41% ; evidence from 3
studies) (Figure 11.8 in Appendix 11). At the same time the effect sizes remained
heterogeneous ranging between -0.23 and 0.49 SMD, suggesting that the type of
training was important. Unfortunately, however, the included studies did not
present much detail on the type of training. Thus, we have to remain careful in the
interpretation of the effects of training in SHGs on women’s family-size decision-
making power.
For mobility, evidence from meta-regression suggested a counter-intuitive finding,
namely that SHGs with a training component had a lower effect on mobility than
studies without a training component (β=-0.15; 95% CI=-0.03, -0.27). However, this
finding is driven entirely by a single study – Pitt et al. (2006) is the only study with
NOTE: Weights are from random effects analysis
Overall (I-squared = 85.9%, p = 0.000)
ID
Study
Desai and Joshi, 2012, India
Desai and Tarozzi, 2013, Ethiopia
Pitt et al., 2006, Bangladesh
Kim et al., 2009 + Pronyk et al., 2006, South Africa
0.26 (-0.04, 0.56)
ES (95% CI)
0.45 (0.25, 0.66)
-0.23 (-0.96, 0.50)
0.06 (-0.04, 0.15)
0.49 (0.25, 0.73)
100.00
Weight
%
28.95
11.06
32.45
27.54
0.26 (-0.04, 0.56)
ES (95% CI)
0.45 (0.25, 0.66)
-0.23 (-0.96, 0.50)
0.06 (-0.04, 0.15)
0.49 (0.25, 0.73)
100.00
Weight
%
28.95
11.06
32.45
27.54
Impact SHGs on Family-Size Decision Making RCTs and Medium Risk of Bias Quasi-Experimental Studies
0-.96 0 .96
75 The Campbell Collaboration | www.campbellcollaboration.org
an emphasis on the effects of SHGs without a training component on women’s
mobility (SMD=0.29, 95% CI=0.19, 0.38). Furthermore, the findings are counter-
intuitive hence we are careful in interpreting them. Figure 11.9 (Appendix 11)
presents the meta-analysis for the effect of SHGs on women’s mobility for studies
with an emphasis on SHGs with a training component. The results show an average
effect size of 0.14 SMD that is statistically significantly different from zero at the 95
per cent confidence level (SMD=0.14, 95% CI=0.06, 0.21; Q=3, Tau-sq=0.00, I-
sq=0%; evidence from 2 studies).
The findings suggested that women’s mobility and women’s family-size decision
making should not be considered as part of the same construct. Thus, in
summarizing the findings the effects of SHGs on social empowerment, we separate
women’s mobility and family-size decision-making (Tables 4.8 and 4.9).
Table 4.8: Summary effects of SHGs on women’s mobility
Description Effect Size Confidence Interval
Randomized controlled trials N/A N/A
Quasi-experimental studies 0.18 SMD 0.06 SMD; 0.31 SMD
RCTs and quasi-experimental studies with medium risk of selection bias
0.18 SMD 0.06 SMD; 0.31 SMD
Quasi-experimental studies with high risk of selection bias N/A N/A
RCTs and quasi-experimental studies with medium risk of selection bias with an emphasis on SHGs that included training
0.14 SMD 0.06 SMD; 0.21 SMD
RCTs and quasi-experimental studies with medium risk of selection bias with an emphasis on SHGs that did not include training
0.29 SMD 0.19 SMD; 0.38 SMD
Table 4.9: Summary effects of SHGs on women’s family-size decision-making
power
Description Effect Size Confidence Interval
Randomized controlled trials 0.31 SMD -0.09 SMD; 0.70 SMD
Quasi-experimental studies 0.06 SMD -0.04 SMD;0.15 SMD
RCTs and quasi-experimental studies with medium risk of selection bias
0.25 SMD -0.03 SMD;0.54 SMD
Quasi-experimental studies with high risk of selection bias
0.53 SMD 0.22 SMD;0.85 SMD
RCTs and quasi-experimental studies with medium risk of selection bias with an emphasis on SHGs that include training
0.41 SMD 0.19 SMD;063 SMD
RCTs and quasi-experimental studies with medium risk of selection bias with an emphasis on SHGs that do not include training
0.06 SMD -0.04 SMD;0.15 SMD
76 The Campbell Collaboration | www.campbellcollaboration.org
The included studies with an emphasis on social empowerment only included one
study with an emphasis on social empowerment that was not included in the meta-
analysis. This paper did not report an effect size but found positive effects on
women’s mobility (Husain et al., 2010), consistent with the meta-analysis.
Furthermore, the distribution of effect sizes in the meta-analysis gives some
indication for a relationship between contextual characteristics and the impact of
women’s self-help groups on women’s family-size decision-making power. We
analyze this distribution of effect sizes more carefully using a narrative analysis
because our sample size did not allow for a stratified meta-analysis or meta-
regression. The study in Ethiopia showed the least convincing evidence for positive
effects on women’s family-size decision making power (Desai & Tarozzi, 2014). At
the same time the self-help group in Rajasthan, India, showed strong effects on
women’s family-size decision-making (Desai & Joshi, 2012). The results suggest
there may be a difference in the effects of self-help groups on women’s social
empowerment across regions. We will further explore this mechanism in the
qualitative analysis.
4.4.3 Political Empowerment
We were able to include 23 quantitative studies which estimated the effects of
women’s self-help groups on women’s political empowerment. Of these, three
included an estimate of women’s political empowerment resulting from SHGs that
we were able to include in our meta-analysis. However, we only included two effect
sizes because including the study of Swendeman et al. (2009) may result in bias due
to the high risk of selection-bias. Furthermore, we were unable to determine the
effect size for one study estimating the impact of SHGs on women’s political
empowerment (table 4.10).
Table 4.10: Measurement of political empowerment
Study Definition of Variable Scale Included in Meta-Analysis?
Deininger and Liu (2013)
Several dummy variables that are associated with women’s voting behavior
Several binary variables
No; not able to estimate effect size
Desai and Joshi (2012)
Several dummy variables that are associated with women’s participation in community meetings and elections
Several binary variables
Yes
Pitt et al. (2006) Several dummy variables that are associated with women’s voting behavior
Index of binary variables
Yes
Swendeman et al. (2009)
Several dummy variables that are associated with women’s voting behavior
Several binary variables
No, high risk of selection-bias
For our meta-analysis to determine the effects of women’s self-help groups on
political empowerment, we decided to pool one RCT and the quasi-experimental
evaluation with a medium risk of selection bias for which we were able to estimate
77 The Campbell Collaboration | www.campbellcollaboration.org
the effect size in one meta-analysis (Pitt et al., 2006; Desai and Joshi, 2012). We
pooled these studies because our previous analyses to determine the effects of SHGs
on economic and social empowerment suggest that studies with a low-or medium
risk of selection bias can be pooled in one meta-analysis without biasing the results.
We did not include the study of Swendeman et al. (2009) with a high risk of
selection-bias in our meta-analysis because the evidence from the meta-analysis on
economic and social empowerment indicated that studies with a high risk of
selection-bias have an upward bias. Although we were not able to gain a nuanced
understanding of the impacts of women’s self-help groups based on the two studies
that we were able to include in meta-analysis, the results suggested that women’s
self-help groups have a positive effect on women’s political empowerment. The
average effect of women’s self-help groups on political empowerment was estimated
as 0.19 standard deviations (SMD=0.19, 95% CI=0.01, 0.36; Q=3, Tau-sq=0.01, I-
sq=71%; evidence from 2 studies) (Figure 4.13). The limited number of studies did
not allow for sensitivity analysis.
Figure 4.13: Effects of women’s self-help groups on political empowerment
The study of Swendeman et al. (2009) also finds positive effects of SHGs on
women’s political empowerment. Although this study was not included in our meta-
analysis because of the high risk of selection-bias, the positive effect on women’s
political empowerment is consistent with the findings from our meta-analysis.
The study of Deininger and Liu (2009) also included an estimate on political
empowerment. However, it remained unclear how political empowerment was
defined in that version of the paper. Furthermore, the published paper (Deininger &
NOTE: Weights are from random effects analysis
Overall (I-squared = 71.2%, p = 0.062)
Desai and Joshi, 2012, India
Pitt et al., 2006, Bangladesh
Study
ID
0.19 (0.01, 0.36)
0.29 (0.12, 0.46)
0.11 (0.02, 0.20)
ES (95% CI)
100.00
42.41
57.59
%
Weight
0.19 (0.01, 0.36)
0.29 (0.12, 0.46)
0.11 (0.02, 0.20)
ES (95% CI)
100.00
42.41
57.59
%
Weight
Impact SHGs on Political Empowerment Based on RCTs and Medium Risk of Bias Quasi-Experimental Studies
0-.456 0 .456
78 The Campbell Collaboration | www.campbellcollaboration.org
Liu, 2013) did not include a focus on political empowerment. It appeared as if the
political empowerment variable from the working paper included elements of social
empowerment. Therefore, the results of the paper, which reported positive effects on
political empowerment, were not included in our meta-analysis. Nonetheless, the
positive effects are consistent with the findings of the meta-analysis.
4.4.4 Psychological Empowerment
Finally, we synthesized the effects of women’s self-help groups on women’s
psychological empowerment across studies, including adverse effects. Of the 23
included quantitative studies, three included an impact estimate on women’s
psychological empowerment that we were able to include in our meta-analysis.
However, we only included two studies in our meta-analysis because including the
study by Swendeman et al. (2009) may result in bias due to the high risk of
selection-bias. (Table 4.11).
Table 4.11: Measurement of psychological empowerment
Study Definition of Variable Scale Included in Meta-Analysis?
De Hoop et al. (2014) Five-point Likert scale ranging from highly disagree to highly agree as a response to the statement “I have control over my own life”
Categorical variable
Yes
Kim et al. (2009) & Pronyk et al. (2006)
Dummy variable that is 1 if the respondent reports to be self-confident
Binary variable
Yes
Swendeman et al. (2009)
Dummy variable that is 1 when the sex worker reports that sex work is valid work
Binary Variable
No, high risk of selection-bias
As in the meta-analysis for political empowerment, we pooled RCTs and quasi-
experimental studies with a medium risk of selection-bias in our meta-analysis for
psychological empowerment (De Hoop et al., 2014; Kim et al., 2009), but we did not
include studies with a high risk of selection-bias (Swendeman et al., 2009).
Although our meta-analysis is only based on two studies, the forest plot in Figure
4.14 indicated that there is major heterogeneity in the effects of women’s self-help
groups on psychological empowerment (SMD=0.02, 95% CI=-0.21, 0.26; Q=1, Tau-
sq=0.00, I-sq=0%; evidence from 2 studies). One study in India did not find positive
effects on psychological empowerment (De Hoop et al., 2014). A second study in
South Africa demonstrated a large point estimate, but the sample size was too small
and consequently the confidence interval too wide to derive strong conclusions
regarding the effect of women’s self-help groups on psychological empowerment
(Kim et al., 2009). Arguably, there is no evidence for positive effects of SHGs on
psychological empowerment based on the studies we included in our meta-analysis.
79 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.14: Effects of women’s self-help groups on psychological empowerment
4.4.5 Intimate Partner Violence and Other Potential Adverse Effects
We also synthesized the adverse effects of women’s self-help groups with a strong
focus on intimate partner violence. Of the 23 included quantitative studies, three
included an impact estimate on intimate partner violence that we were able to
include in our meta-analysis, and one estimated the impact on partner violence, but
we were not able to determine the effect size of this study (Table 4.12). However, as
in our previous meta-analyses with few studies we do not include studies with a high
risk of selection-bias in the meta-analysis (Ahmed, 2005).
Table 4.12: Measurement of intimate partner violence
Study Definition of Variable Scale Included in Meta-Analysis?
Ahmed (2005) Dummy variable that is 1 if the respondent reports that she has been a victim of any type of violence
Binary No, high risk of selection-bias
De Hoop et al., (2014)
Five-point Likert scale ranging from highly disagree to highly agree as a response to the statement “Men are entitled to beat their women in certain occasions”
Categorical variable
Yes
Husain et al. (2010)
Several variables associated with women’s tolerance of domestic violence, which are 0 if the woman thinks violence is not justified, 0.5 if the woman is uncertain and 1 if the woman thinks violence is justified
Several categorical variables
No; not able to estimate effect size
Kim et al. (2009) &
Several dummy variable that are 1 if the respondent condones intimate partner violence
Several binary variable
Yes
NOTE: Weights are from random effects analysis
Overall (I-squared = 0.0%, p = 0.363)
Kim et al., 2009 + Pronyk et al., 2006, South Africa
De Hoop et al., 2014 India
ID
Study
0.02 (-0.21, 0.26)
0.50 (-0.55, 1.56)
0.00 (-0.24, 0.24)
ES (95% CI)
100.00
4.84
95.16
Weight
%
0.02 (-0.21, 0.26)
0.50 (-0.55, 1.56)
0.00 (-0.24, 0.24)
ES (95% CI)
100.00
4.84
95.16
Weight
%
Impact SHGs on Psychological Empowerment RCTs and Medium Risk of Bias Quasi-Experimental Studies
0-1.56 0 1.56
80 The Campbell Collaboration | www.campbellcollaboration.org
Pronyk et al. (2006)
Our theory of change suggests that women’s self-help groups might have adverse
consequences, in the sense that domestic violence could increase as a result of
participation in women’s self-help groups. However, the meta-analysis of the effects
of women’s self-help groups on attitudes toward domestic violence, did not show
evidence for adverse effects of women’s self-help groups on attitudes toward
domestic violence (Figure 4.15). As in our meta-analyses for political and
psychological empowerment we only pool RCTs and studies with a medium risk of
selection-bias in our meta-analysis (De Hoop et al., 2014; Kim et al., 2009).
Although the point estimate suggests a positive effect of SHGs on positive attitudes
towards domestic violence the relationship is not statistically significant
(SMD=0.07, 95% CI=-0.06, 0.20; Q=0, Tau-sq=0.00, I-sq=0%; evidence from 2
studies). Arguably, our meta-analysis did not allow for a nuanced understanding of
the effect of women’s self-help groups on intimate partner violence, because we only
found two studies with a low or medium risk of selection-bias that could be included
in the meta-analysis. More rigorous evidence about the effect of women’s self-help
groups on intimate partner violence is clearly needed. However, at this moment our
meta-analysis does not show evidence for adverse effects of women’s self-help
groups via a contribution to intimate partner violence.
Figure 4.15: Effects of women’s self-help groups on intimate partner violence
NOTE: Weights are from random effects analysis
Overall (I-squared = 0.0%, p = 0.600)
Study
Kim et al., 2009 + Pronyk et al., 2006, South Africa
ID
De Hoop et al., 2014 India
0.07 (-0.06, 0.20)
0.04 (-0.13, 0.21)
ES (95% CI)
0.11 (-0.09, 0.32)
100.00
%
59.79
Weight
40.21
0.07 (-0.06, 0.20)
0.04 (-0.13, 0.21)
ES (95% CI)
0.11 (-0.09, 0.32)
100.00
%
59.79
Weight
40.21
Impact SHGs on Domestic Violence Based on RCTs and Medium Risk of Bias Quasi-Experimental Studies
0-.321 0 .321
81 The Campbell Collaboration | www.campbellcollaboration.org
In addition to the studies we included in our meta-analysis, two other studies also
presented impact estimates on intimate partner violence. First, Ahmed (2005)
presented evidence for an adverse but non-significant effect of women’s self-help
group membership on the likelihood of female respondents having encountered
violence. This study was not included in the meta-analysis because of the high risk of
selection-bias. Second, Husain et al. (2010) presented findings that suggested a
negative effect of women’s self-help group membership on women’s tolerance of
domestic violence. However, we were not able to estimate the effect size of this study
because point estimates were not reported. Furthermore, the study was rated as
having a high risk of selection-bias.
Our included studies only contained one study that focused on other adverse
consequences of women’s self-help groups. De Hoop et al. (2014) argued that, on
average, women’s self-help groups might not have adverse consequences for
subjective well-being or happiness, but, at the same time, they found strong negative
effects on happiness of women’s self-help group members in relatively conservative
areas. De Hoop et al. (2014) argued these negative effects occurred because of social
sanctioning of women who show autonomous behavior and because of the internal
psychological struggles of women who are autonomous in a patriarchal context
where this is not considered appropriate behavior for a woman. The absence of
average negative effects in the full sample and the strong negative effects in areas
with relatively conservative gender norms indicate that adverse consequences of
women’s self-help groups may be clouded by heterogeneities in the impact
estimates. Alternatively, negative effects may also be underreported or researchers
may not focus on collection of data on adverse outcomes. We have to be cautious in
interpreting this result, however, because the finding was based on only one study
with a medium risk of selection bias and a high risk of spillovers. Thus, the findings
of the study might not be internally or externally valid, although they were
supported by qualitative accounts of women’s empowerment trajectories reported in
the same study.
Table 4.13 summarizes the results of all meta-analyses with an emphasis on political
and psychological empowerment or intimate partner violence.
Table 4.13: Summary effects of SHGs on women’s political and psychological
empowerment and intimate partner violence
Description Effect Size Confidence Interval
RCTs and quasi-experimental studies with medium risk of selection bias focusing on political empowerment
0.19 SMD
0.01 SMD; 0.36 SMD
RCTs and quasi-experimental studies with medium risk of selection bias with focusing on psychological empowerment
0.02 SMD
-0.21 SMD; 0.26 SMD
RCTs and quasi-experimental studies with medium risk of selection bias focusing on intimate partner violence
0.07 SMD -0.06 SMD; 0.20 SMD
82 The Campbell Collaboration | www.campbellcollaboration.org
4.3.5 Publication bias assessment
We relied on funnel plots to determine the potential for publication bias of studies
that focused on economic and social empowerment. As discussed above, the number
of studies that focused on political and psychological empowerment was not
sufficient to determine the potential for publication bias of studies that focused on
these topics. For social empowerment we decided not to test for publication bias for
women’s family-size decision making power and mobility separately, despite the fact
that our meta-analysis suggests that these two empowerment components can be
considered different constructs, because this would have resulted in a strong
reduction of statistical power to reject the null hypothesis of no publication bias.
Figure 4.16 presents a funnel plot for studies that focused on economic
empowerment with a low or medium risk of selection-bias. The basic idea of a funnel
plot is that publication bias is most likely when the effect sizes of studies do not
follow a normal distribution. As can be clearly seen in the figure, the effects on
economic empowerment are not normally distributed. Instead, it appears as if the
results are skewed to the right. Hence, the funnel plot suggests that there might be
publication bias in the studies that estimated impacts on economic empowerment.
For social empowerment we find a similar pattern with results skewed to the right.
Thus, there may also be publication bias for impact evaluations that focus on the
effects of women’s self-help groups on social empowerment. Funnel plots can be
interpreted in multiple ways, however, so we should be careful in interpreting the
figure. We can only say there is potential for publication bias in the impact estimates
on economic empowerment.
Figure 4.16: Funnel plot of economic empowerment outcome
0
.1
.2
.3
.4
Sta
nd
ard
err
or
-1 -.5 0 .5 1Effect estimate
Studies
1%
5%
10%
83 The Campbell Collaboration | www.campbellcollaboration.org
Figure 4.17: Funnel plot of social empowerment outcome
We formally tested for the potential of publication using Egger’s test. For both
economic and empowerment we found no formal evidence for publication bias
based on this test. For economic empowerment the point estimate for publication
bias is positive but the results are not statistically significant (β=2.32, S.E.=1.58,
p=0.20). For social empowerment the Egger test indicated no evidence for
publication bias (β=0.25, S.E.=1.33; p=0.86). Hence, although there are indications
of publication bias in the studies that focused on economic empowerment we found
no formal evidence for publication bias based on the Egger test.
Nevertheless, our risk of bias assessment did present some evidence for publication
bias. For example, we found two studies that did not report point estimates because
the results were not statistically significant (Mahmud, 1994; Steele et al., 1998). This
indication of outcome and analysis reporting biases may indicate that the positive
impacts we found could be slightly overestimated. Similarly, only a few of our
studies (Ahmed, 2005; De Hoop et al., 2014; Husain et al., 2010; Kim et al., 2009)
assessed adverse consequences of SHGs. The relatively low number of studies
focusing on adverse consequences may indicate reporting bias. However, despite the
potential for outcome and analysis reporting bias, we did not find evidence for
differential effects for studies with high outcome and analysis reporting bias.
0
.2
.4
.6
.8
1
Sta
nd
ard
err
or
-2 -1 0 1 2Effect estimate
Studies
1%
5%
10%
84 The Campbell Collaboration | www.campbellcollaboration.org
4.5 SYNTHESIS OF QUALITATIVE STUDIES
The meta-ethnographic analysis of the qualitative studies focused on women’s
explanations of empowerment outcomes (review objective 2). The 11 studies
included in the qualitative analysis came from SHGs in South Asia (Bangladesh,
India and Nepal), Bolivia and Tanzania. Table 4.14 and Table 4.15 summarize the
findings from the qualitative studies after relying on the meta-ethnographic
approach. The following descriptions of the four major outcome categories
(economic, social, political, and psychological empowerment) emerged from
women’s accounts of their self-help group experiences from the 11 contributing
studies. A table of additional quotes for each theme is available in Appendix 12.
85 The Campbell Collaboration | www.campbellcollaboration.org
Table 4.14: Summary of evidence in qualitative studies
THEME Dahal 2014, Nepal
Kabeer 2011, Bangladesh
Kilby 2011, South India
Knowles 2014, South India
Kumari 2011, South India
Maclean 2012, Bolivia
Mathrani 2006, South India
Mercer 2002, Tanzania
Pattenden 2011, South India
Ramachandar 2009, South India
Sahu 2012, South India
Psychological Empowerment
Agentic Voice x x x x x x
Household Negotiations
x x x x x
Impact on Domestic Disputes
x x x x x x x
Social Empowerment
Improved Networking x x x x x x x
Solidarity x x x x
Community Respect x x x x x
Economic Empowerment
Financial Skills x x x x x x x
Financial Experience x x x x x x x
Political Empowerment
Broader Social Action x x x x x x x x
86 The Campbell Collaboration | www.campbellcollaboration.org
Limits of Political Context
x x x
Adverse Outcomes
Barriers to Participation
x x x
Disappointment x x x x x
Corruption x x x x
Stigma x x x
87 The Campbell Collaboration | www.campbellcollaboration.org
Table 4.15: Summary of findings from qualitative studies
Theme Sample Quotes Contributing Studies
Confidence in Evidence
Explanation of Confidence in Evidence
Psychological Empowerment
Agentic Voice: Women from South Asia reported feeling more capable of speaking in front of others. Women experienced this by speaking in front of their peers at their group meetings. As groups matured and began to get involved in community development projects, women also talked about feeling capable of speaking in front of others, such as extended families, authorities, and community leaders.
“One of the things I have learned is to be able to speak in front of a group of five people without shivering.” Kumari, 2011, South India
Dahal 2014, Kabeer 2011, Kilby 2011, Kumari 2011, Mathrani 2006, Ramachandar 2009
High Thick data from 6 studies; Only from South Asia; quality was high for 4 studies and medium for 2 studies.
“My confidence level is increasing. Before, I was afraid to speak out what I disliked, but now I am not dependent on anyone and I can speak my thoughts and I don’t care whether someone likes it or not.” Dahal, 2014, Nepal
Participation in Household Decisions: Women discussed the process of gaining acceptance from husbands and in-laws to participate in SHGs. Then, over time, they described gaining respect from husbands and extended family for their contributions and became part of the household decision-making.
“After two years, they [husband and in-laws] understood the value of the women’s groups and remained silent.” Ramachandar, 2009, South India
Dahal 2014, Kabeer 2011, Kumari 2011, Mercer 2002, Ramachandar 2009, Sahu 2012
High Thick data from 6 studies; 5 from South Asia, 1 from Tanzania; quality was high for 2 and medium for 4.
“Being allowed to have money and decide on how to spend it has brought us development in our household and now husbands give us the freedom to do our own things.” Mercer, 2002, Tanzania
Impact on Domestic Disputes: Women reported that their participation had an effect on domestic disputes and violence including both verbal and physical abuse. Women reported an initial increase in disputes or violence but that they eventually gained respect from husbands and in-
"My husband used to beat me when I became a member of the sangha. He used to manhandle me when I returned home from the meetings. His parents instigated him to beat me. But I stood in silence and today he dare not touch me.” Ramachandar, 2009, South India
Dahal 2014, Kabeer 2011, Kilby 2011, Knowles 2014, Kumari 2011, Mathrani 2006,
High Thick data from 8 studies; Only from South Asia; quality was high for 5 and medium for 3.
88 The Campbell Collaboration | www.campbellcollaboration.org
laws by bringing in income to the household and that they fought less with their husbands. They also reported that their SHGs took action against domestic violence in their communities.
“You cannot come drunk and batter me, my SHG will question you if you touch me, you should be prepared to answer them.” Kumari, 2011, South India
Ramachandar 2009, Sahu 2012
Social Empowerment
Improved Networking: Women SHG members had the confidence to work with local authorities, village leaders, and law enforcers to make positive changes in their communities. These experiences emboldened the women to address authorities when a social issue came up or when they needed support for a community development project. This was a profound change from being confined to the domestic sphere and speaking only to family and close neighbors.
“SHG members complain if a tap is broken or if there is stagnant water ... they bring this to the panchayat [village leader] president’s attention issues in the community ... if they have other difficulties they go to government officials now.” Knowles, 2014, South India
Kabeer 2011, Kilby 2011, Knowles 2014, Kumari 2011, Mathrani 2006, Pattenden 2011, Sahu 2012
High Thick data from 7 studies; only from South Asia; quality was high for 4 and medium for 3.
“The women themselves insisted on dealing with the tractor owners directly and ‘held out’ for three weeks before the tractor owners agreed to deal with the women directly. It was the close interaction with staff at all levels, which gave the women the confidence to deal with higher caste village people in this way.” Kilby, 2011, South India
Solidarity: Women reported feeling mutual support within their groups and feeling as though they could speak as a collective voice. A sense of solidarity enabled women to make meaningful decisions and to enact positive change in their lives and communities.
“One stick can be broken, a bundle of sticks cannot. It is not possible to achieve anything on one’s own. You have no value on your own. Now if I am ill, my [SHG] members will look after me.” Kabeer, 2011, Bangladesh
Dahal 2014, Kabeer 2011, Kumari 2011, Mathrani 2006
Moderate Thick data from 4 studies; only from South Asia; quality was high for 2 and medium for 2.
“If we disapprove of something, we are able to express our opinions to the larger community as we have a collective voice.” Mathrani, 2006, South India
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Community Respect: Women described walking confidently through their villages and feeling respected by their peers and their leaders. They expressed feeling that they were no longer solely housewives but community actors who had influence over their village politics.
“The society’s view upon being a SHG member has changed. Before it was against the social norms to go out of a house but now society praises women who are involved in SHGs.” Dahal, 2014, Nepal
Dahal 2014, Kabeer 2011, Kumari 2011, Sahu 2012, Ramachandar 2009
High Thick data from 5 studies, Only from South Asia; quality was high for 2 and medium for 3.
“The biggest benefit of the [SHG] is that we get prestige and honour in our community; we gain experience going to the bank and meeting with officials.” Ramachandar, 2009, South India
Economic Empowerment
Financial Skills: Women reported feeling empowered by the newness of handling money. Many of the women had never participated in the buying and selling of goods and had never been allowed to manage the household accounting. With the new access to credit, women were suddenly in the role of the money manager. Women reported that they gained a sense of self-reliance as a result of having access to money, making decisions about buying and selling, and completing transactions with that money
"The fear of handling money is gone." Kumari, 2011, South India
Dahal 2014, Kabeer 2011, Kumari 2011, Maclean 2012, Mercer 2002, Ramachandar 2009, Sahu 2012
High Thick data from 7 studies across regions (5 from South Asia, 1 from Tanzania and 1 from Bolivia); quality was high for 2 and medium for 5. "Being allowed to have money and decide on
how to spend it has brought us development in our households and now husbands give us the freedom to do our own things." Mercer, 2002, Tanzania
Financial Inexperience: Being in charge of finances was a new experience for most women and the women reported feeling unsure about their financial decision making abilities. Some of the SHGs offered training around such topics as income generation and savings. But because women were making decisions in front of their community members, they felt there was a great deal at stake to make sound choices.
“The interest rate is really high. Don Pedro—my husband—tells me off: ‘Why are you just working for that [the credit]. You’re just working for the bank, and the interest is really expensive!’” Maclean, 2012, Bolivia
Knowles 2014, Kumari 2011, Maclean 2012, Mathrani 2006, Pattenden 2011, Ramachandar 2009
Moderate Thin description from 6 studies from 2 regions (5 from South Asia and 1 from Bolivia); quality was high for 4 and medium for 2.
"The men say, 'What kind of structure have these women constructed? They are like monkeys, if we hit their home it will collapse.'" Mathrani, 2006, South India
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Political Empowerment
Catalyzing Social Action: Women described their participation in a SHG as a “stepping stone” toward wider social participation but not necessarily a political act in itself. Women reported that participation in SHGs did expose them to the concept of women’s rights through participation in social activities and it did give them political capital the ability to speak out on political issues such as accountability. Women reported that some members of SHGs went on to become local political leaders.
“In the previous election, the MLA candidate had promised to build a road but he did not. When he came for campaigning this time, we questioned him for not keeping his promise and we didn't vote him either.” Sahu, 2012, South India
Dahal 2014, Kabeer 2011, Kilby, 2011 Knowles 2014, Kumari, 2011 Mathrani 2006, Sahu 2012
High Thick description from 7 studies all from South Asia; quality was high for 4 and medium for 3.
“SHG members [have] become councillors, government officials ... those elected [in] six out of 15 wards are women and members of elected panchayat bodies. They advanced their skills and were respected by the community." Knowles, 2014, South India
Understanding the Political Context: SHG members were able to identify the limits to their "empowerment" and described SHGs facing barriers to affecting change in their community through even small political acts. The context within which groups operated “restricted the capacity for political action.” Women talked about feeling that awareness of rights was only an important first step and they still had a long way to go before women gained property and reproductive rights. Women agreed that their domestic role of women was still primary.
“Empowerment? There has not been complete empowerment. More factors are needed like equal wages. I would say that only 5 to 10% of empowerment has happened.” Kumari, 2011, South India
Kabeer 2011, Kumari 2011, Mathrani 2006, Pattenden 2011, Ramachandar 2009
Moderate Thin description from 5 studies all from South Asia; quality was high for 3 and medium for 2.
"Women are still tethered to domesticity and men still regarded women as below them." Kabeer, 2011, South India
Adverse Outcomes
Barriers to Participation: Women described barriers to participation specifically for marginalized groups such as lower castes or the very poor. This finding is likely underreported
“Some women don't join because they feel inferior, they think that members are rich, can afford things and can be close to the Church, they are in good positions.” Mercer, 2002, Tanzania
Dahal 2014, Knowles 2013, Mercer 2002, Mathrani 2006
Moderate Thin description from 4 studies from two regions (3 from South Asia and 1 from Tanzania); quality was high for 3 and medium for 1.
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because studies focused on the narratives of participants versus non-participants.
“The issue of selection bias can be agreed to a certain extent acknowledging to the fact that very poor people cannot afford the membership fee and enough time for group activities.” Dahal, 2014, Nepal
Disappointment: Women described a degree of disappointment when their groups did not deliver on perceived promises such as solving social problems in their villages like alcoholism. Another source of disappointment occurred when women gained new awareness about rights but were not able to enact them or when their group took on new responsibilities but in the end did not have the authority or financial power to make changes.
"Other women are discouraged because it is almost four to five years since we contributed the money for the cows and up to now we haven't seen any good profit." Mercer, 2002 Tanzania
Dahal 2014, Kabeer 2011, Kumari 2011, Maclean 2012, Mercer 2002
Moderate Thin description from 5 studies from 3 regions (South Asia, Bolivia and Tanzania); quality was high for 1 and medium for 4.
“SHGs operate at very low cost, have a small fund, raise little interest so we cannot accomplish bigger projects and this is our weakness.” Maclean, 2012, Bolivia
Mistrust and Corruption: Women reflected on negative experiences such as mistrust and corruption of their group and told stories about corruption they had heard about in other groups specifically of leaders stealing group funds
“I don’t like [to be treasurer]. It’s dangerous. The money can disappear, you can get confused. Even Dona Feliza [a younger woman who was educated in la Paz] can get a little confused sometimes. And they talk about the treasurer and accuse her of things.” Maclean, 2012, Bolivia
Knowles 2014, Maclean 2012, Dahal 2014
Low Thin description from 3 studies from two regions (South Asia and Bolivia); quality was high for 2 and medium for 1.
“Accounts are not maintained. The leaders of SHGs are heard to have lent the saved amounts to others at high interest rates for personal benefit.” Dahal, 2014, Nepal
Stigma: Membership in SHGs had negative associations and women faced public shame or discrimination, especially during the early days of the formation of the group. Women reported hearing stories of other SHG members being
"Upper castes say, 'These women attend meetings and visit the panchayat to get money. They are trying to usurp the position of the gowda and take control of the village.’" Mathrani, 2006, South India
Mathrani 2006, Pattenden 2011, Ramachandar 2009
Moderate Thick description from 2 studies and thin description from 2 study all from South Asia; quality was high for 3 and medium for 1.
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stoned for membership or SHG women were seen as trouble-makers accused of trying to take over the local council.
The men used to make comments such as, these women are doing “tamasha” (showing off) and they are going to close down our sangha after a few days. But we did not worry about those comments.” Ramachandar, 2009, South India
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Psychological empowerment
In contrast to the quantitative literature, much of the qualitative literature on
individual-level empowerment focuses on self-confidence and self-esteem, and
suggests women participating in SHGs feel psychologically empowered. The 11
contributing qualitative studies included in this review suggest specific aspects of
individual-level change which were experienced by women self-help group
members.
Agentic voice: One of the dominant themes from six studies is that women self-help
group members reported feeling more capable of speaking in front of others. First,
women experienced this by speaking in front of their peers at their group meetings.
As groups matured and began to get involved in community development projects,
women also talked about feeling capable of speaking in front of others, such as
extended families, authorities, and community leaders (Dahal, 2014; Kabeer, 2011;
Kilby, 2011; Kumari, 2011; Mathrani & Pariodi, 2006; Ramachandar & Pelto, 2009).
Participation in household negotiations: Another emergent theme involved intra-
household dynamics, which was mentioned in six studies (Dahal, 2014; Kabeer,
2011; Kumari, 2011; Mercer, 2002; Ramachandar & Pelto, 2009). At first, women
reported the process of gaining acceptance from husbands and in-laws to participate
in SHGs. Furthermore, women described gaining respect over time from husbands
and extended family and becoming decision-makers within their households
following their membership in SHGs.
Domestic disputes: Women in eight studies reported how their participation in
SHGs had contributed to domestic disputes and violence including both verbal and
physical abuse (Dahal, 2014; Kabeer, 2011; Kilby, 2011; Kumari, 2011; Mathrani &
Pariodi, 2006; Ramachandar & Pelto, 2009; Sahu & Singh, 2012). Women from
three studies reported an initial increase in disputes or violence but said that they
eventually gained respect from husbands and in-laws by bringing in income to the
household. These women also reported fighting less with their husbands (Kumari,
2011; Mathrani & Pariodi, 2006; Ramachandar & Pelto, 2009). In two other studies
women reported that they experienced a decrease in disputes and conflict between
husbands and wives (Knowles, 2014; Sahu & Singh, 2012). In all eight studies,
women described how SHG members put social pressure on men to stop beating
wives and would show up in groups to support women who had been beaten. The
interviewed women felt these activities decreased domestic violence in their
communities.
Social empowerment
The literature around empowerment talks about social capital accumulation as a
result of participation in SHGs. We found three main themes that emerged within
the context of social capital that explain this phenomenon in more detail.
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Networking: An important theme discussed by women in seven studies was that not
only were women SHG members more confident speaking in front of others, but the
women also felt comfortable working with local authorities, village leaders, and law
enforcers to make positive changes in their communities (Kabeer, 2011; Kilby, 2011;
Knowles, 2014; Kumari, 2011; Mathrani & Pariodi, 2006; Pattenden, 2011; Sahu &
Singh, 2012). Women’s perceptions suggest that these experiences emboldened
them to address authorities when a social issue came up or when they needed
support for a community development project.
For these women SHG members, networking experiences represented a profound
change from being confined to the domestic sphere and speaking only to family and
close neighbors. In one group in India, women had to negotiate with formal banking
institutions. The women reported that these institutions at first refused to give them
loans, but the women went up the chain of authority to the national bank for rural
development and their loans were released (Kumari, 2011).
Women suggested that this type of networking was useful in getting small projects
completed, and, in four studies, women report that they capitalized on relationships
and progressed from holding leadership positions with their groups to holding
leadership positions within the community (Knowles, 2014; Kilby, 2011; Kumari,
2011; Mathrani & Pariodi, 2006).
Solidarity: Another important theme was the empowerment that came from group
solidarity. Women’s experiences suggested that knowing that their group is
supporting them enabled women to make meaningful decisions and to enact positive
change in their lives. This boldness to make change as a result of solidarity was
reported with respect to situations within the household or the extended family.
Four studies reported on women’s perspectives about group solidarity (Dahal, 2014;
Kabeer, 2011; Kumari, 2011; Mathrani & Pariodi, 2006).
The boldness of women was particularly strong when women talked about how their
husbands treated them. Women in three studies (Kabeer, 2011; Kumari, 2011;
Mathrani & Pariodi, 2006) reported feeling that they now had recourse from the
group for husbands who committed such acts as domestic violence and heavy
drinking.
Community respect: Similar to this sense of solidarity that was apparent, women
reported feeling that being a part of their SHG gave them clout within their
communities in five studies (Dahal, 2014; Kabeer, 2011; Kumari, 2011; Sahu &
Singh, 2012; Ramachandar & Pelto, 2009). Women described walking confidently
through their villages and having the courage to approach authorities in a group
whereas before they had not felt this way. The women felt more able to participate in
community decision-making, and they felt respected by their peers and their leaders.
The women were no longer solely housewives but community actors.
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Economic Empowerment
Financial skills and independent decision-making: A theme across seven studies
was that women reported feeling empowered by the newness of handling money.
Many of the women had never participated in the buying and selling of goods and
had never been allowed to manage the household accounting before their SHG
membership. With the new access to credit following SHG membership, women
were suddenly in the role of the money manager. Although the learning curve was
steep for some, most women reported they gained a sense of self-reliance as a result
of having access to money, making decisions about buying and selling, and
completing transactions with that money (Dahal, 2014; Kabeer, 2011; Kumari, 2011;
Maclean, 2012; Mercer, 2002; Ramachandar & Pelto, 2009; Sahu & Singh, 2012.).
One interesting finding from two studies was that women stated that they were
putting money aside specifically for their daughters’ education (Mathrani & Pariodi,
2006; Sahu & Singh, 2012).
Financial experience and handling money: Because handling money was a new
experience for most women, women in six studies reported feeling unsure about
their financial decisions (Knowles, 2014; Kumari, 2011; Maclean, 2012; Mathrani &
Pariodi, 2006; Pattenden, 2011; Ramachandar & Pelto, 2009). Some of the SHGs
offered training around such topics as income generation and savings. But because
women were making decisions in front of their community members, they felt there
was a great deal at stake to make sound choices.
In three self-help groups, women reported not feeling prepared to make certain
financial decisions related to their individual or group projects (Maclean, 2012;
Mathrani & Pariodi, 2006; Ramachandar & Pelto, 2009). In one SHG in Bolivia, the
women reported that men saw their participation in the SHG as foolish because they
were not knowledgeable enough with money to be able to benefit from microfinance
services (Maclean, 2012). In another SHG in India, the community was initially
discouraging and ready to scorn at any misstep (Mathrani & Pariodi, 2006). But in
this case, women reported using the public embarrassment to generate greater
determination to fix the construction and build a stronger structure. The women
reported spending considerable time researching building materials and using their
networking skills to find proper builders and building materials in order to redo
their community center.
Political empowerment
Catalyzing broader social action: In seven studies, women described their
participation in a SHG as a “stepping stone” (Mathrani & Pariodi, 2006) toward
wider social participation but not necessarily a political act in itself. Participation in
SHGs did expose the women to women’s rights through participation in social
activities and it did give them political capital through networking (Kumari, 2011;
Dahal, 2014) and encouraged them to speak out on political issues such as
transparency and accountability (Knowles, 2014; Sahu & Singh, 2012). In addition,
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women who go on to participate in local village government indicate that
participating in SHGs provided the support and grounding for them to be able to
take leadership positions in government (Kilby, 2011).
Understanding political context: In three settings, women talked about
understanding what they could and could not change in their communities. Women
were able to identify barriers to affecting change in their community through even
small political acts (Mathrani & Pariodi, 2006; Pattenden, 2011; Ramachandar &
Pelto, 2009). The context within which groups operated “restricted the capacity for
political action” (Pattenden, 2011, p.483). In two other settings, women reported
that the gradual acceptance by husbands and community member gave way to
broader acceptance and respect, which lent strength to their political efforts
(Mathrani & Pariodi, 2006; Ramachandar & Pelto, 2009).
But in one case, women reported that changing the status of women in their society
was not their priority and not on their stated agenda (Mathrani & Pariodi, 2006). In
this case it appeared as if women SHG members remained focused on poverty
reduction through income-generation and community development—not directly
challenging gender norms or women’s status in society. The author of the study
reported that things like networking and household decision-making constituted
micro-political processes. The author suggests that in this specific case SHG
participation did not change the station in life of women: the women were still
“tethered to domesticity” and men still regarded women as below them (Kumari,
2011).
In one case, women talked about feeling more aware of their rights but awareness
was only an important first step and the women still had a long way to go before
women gain property and reproductive rights and the domestic role of women was
still primary (Kabeer, 2011).
And as Kabeer (2011) stated when discussing this theme observed in the data from
her study:
“In social terms, marriage is still the only conceivable pathway to full
adulthood for women, particularly in rural areas. In economic terms, it
marks the necessary transition from their dependence on fathers to
dependence on husbands and ultimately on sons. On both counts, women
had a strong stake in shoring up rather than undermining the institution,
however abusive the relationships involved” (p. 519).
Adverse Outcomes
Barriers to Participation: Three studies reported that women talked about barriers
to participation including economic and social standing (Dahal, 2014; Mercer, 2002;
Mathrani & Pariodi, 2006). Specifically, lower class women were excluded from
“high class” SHGs and lower caste members were not allowed to mix into upper
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caste groups due to discrimination. In Tanzania, women reported that wealthier
women were more likely than poor women to join SHGs. Women’s perceptions
suggest that to poor women, the SHG was a status symbol and served to reinforce
the idea that wealthier or less poor women had more access to financial services,
social capital, and community respect than poorer women (Mercer, 2002). In India,
issues of caste and religion came up in terms of participation and groups of the same
caste joined together to avoid conflict. But due to limited funding, some groups of
the same caste had to wait or did not get funding for their SHG (Mathrani & Pariodi,
2006).
Disappointment: Five studies reported that some women felt a degree of
disappointment when their groups did not deliver on perceived promises such as
solving social problems in their villages such as alcoholism (Mercer, 2002) and
challenging cultural norms (Dahal, 2014; Kabeer, 2011). Another source of
disappointment occurred when women gained new awareness about rights but were
not able to enact them (Kumari, 2011) or when their group took on new
responsibilities but in the end did not have the authority or financial power to make
changes (Maclean, 2012).
Mistrust and Corruption: In three studies, women reflected on negative experiences
about mistrust and corruption of their group or stories about corruption in other
groups particularly stories of leaders stealing group funds (Knowles, 2014; Maclean,
2012; Dahal, 2014).
Stigma: Membership in two SHGs had negative associations and women reported
facing public shame or discrimination especially during the formation of the groups.
This experience of discrimination was reported much less than experiences of
increased respect by community member. But importantly, women reported hearing
stories of women being stoned for membership (Pattenden, 2011) or SHG women
were seen as trouble-makers accused of trying to take over the local council
(Mathrani & Pariodi, 2012).
4.6 INTEGRATED SYNTHESIS
The quantitative synthesis suggests that SHGs have positive effects on women’s
economic, social, and political empowerment ranging from 0.06–0.41 standard
deviations. We did not find quantitative evidence for positive effects of SHGs on
psychological empowerment. However, we found that women perceive positive
contributions of SHGs to psychological empowerment in the synthesis of the
qualitative research. Thus, either the quantitative studies do not adequately measure
psychological empowerment or the women’s perceptions are biased due to various
cognitive biases, such as the fundamental error of attribution or the tendency for
people to attribute changes to programs rather than contextual characteristics
(White & Phillips, 2012).
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The quantitative evidence does not suggest strong adverse impacts of self-help
groups on indicators such as disappointment, stigma, or domestic violence, although
we were only able to meta-analyze the impact of self-help groups on domestic
violence. Findings from the qualitative research suggest that women perceive SHGs
as having the potential to reduce domestic violence as a result of some combination
of the following: 1) improved economic stability, 2) increased respect of wives by
husbands, 3) increased self-confidence of women, 4) exposure to human rights and
gender training and 5) enforcement from SHG members to reduce violence within
households. These perceptions on domestic violence were one of the strongest
themes drawn from eight contributing qualitative studies, although these studies
were all conducted in South Asia. However, the quantitative meta-analysis examined
the effects of women’s self-help groups on attitudes toward domestic violence and
the results neither showed evidence for adverse effects of women’s self-help groups
on attitudes toward domestic violence nor evidence for the potential of SHGs to
reduce domestic violence. Thus, we need to be careful in interpreting this result.
Nonetheless, our findings certainly do not suggest that there is evidence for
increasing the likelihood 0f domestic violence for SHG participants.
Furthermore, self-help groups may have stronger effects on economic empowerment
and women’s family-size decision-making power when the self-help group includes a
training component. However, we should be careful in the interpretation of this
finding because both quantitative and qualitative studies present insufficient details
about the contents of the training in SHGs. In the quantitative studies, health
education training and training on business and entrepreneurial skills were the most
prevalent, but we were not able to distinguish between the effects of different types
of training in a meta-analysis because of the limited number of studies.
Although the quantitative analysis did not allow for a rigorous identification of
contextual moderators of heterogeneous effects, the qualitative synthesis suggests
various reasons for why women do not experience empowerment as a result of
women’s self-help groups under all circumstances. The first barrier toward an
empowering experience resulting from self-help groups that was identified by
women SHG members is a barrier to participation in self-help groups. Women SHG
members suggest that the poorest of the poor, lower caste members or other
marginalized groups may not always have the possibility to participate in SHGs. This
perception of women SHG members suggests that the theory of change underlying
self-help group interventions we proposed should start even before female
participation in economic or livelihood self-help groups. Several assumptions need
to be fulfilled before women even start participating in self-help groups. However,
we should be careful in interpreting the result about participation because of the
limited potential of qualitative studies to determine causal effects. This finding,
nonetheless, reinforces the call of De Hoop and Menon (2014) to more
systematically analyze participation in development programs. They argue that:
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…while implementing a women’s self-help group programme, it would be
important to consider the possibility that information about the existence of
the programme may not reach potential participants. It is also likely that the
women may consider attendance in meetings to have a big opportunity cost.
They may have to give up several hours of work on their farm to attend a self-
help group meeting. Assumptions about participation may also run counter
to what a woman is able to do in her community. Women may have to break
gender related social norms to attend this meeting unaccompanied by their
spouse or male relative. (De Hoop & Menon, 2014)
The latter argument relates to a different conclusion from the qualitative research.
Here, it is interesting to see that women’s perspectives suggest that women in
Bolivia and Tanzania encounter more resistance from the community when they
participate in self-help groups than women in South Asia. Women’s perspectives
from South Asia suggest that the initial resistance of other community residents to
participation of women in self-help groups and the resulting empowerment process
fades out after women are self-help group members for a longer amount of time.
This finding suggests that the maturity of self-help groups might be an important
additional moderator for achieving effects on women’s empowerment. With respect
to social empowerment, the quantitative evidence suggests this may be true. We
found stronger effects of women’s self-help groups on women’s family-size decision-
making power in the context of India, where self-help groups are well-established,
than in the context of Ethiopia, where self-help groups are less well-established.
However, we have to exercise caution in interpreting this finding because there may
be factors that confound this result such as reporting bias and cross-cultural
misinterpretation. In addition, the number of studies that discuss backlash from the
community is relatively small.
In general, qualitative studies do not give sufficient attention to the identification of
causal effects and quantitative studies do not emphasize enough the importance of
potential moderators in the design of SHG programs. Too often qualitative studies
present information about the empowering experience of women in self-help groups
without focusing attention on issues like self-selection in self-help groups. At the
same time, our meta-analysis suggests that self-selection in self-help groups
complicates counterfactual analysis tremendously. Studies with a high risk of
selection bias overestimate the impact of self-help groups relative to studies with a
medium or low risk of bias. Furthermore, quantitative studies do not present
sufficient detail regarding the specifics of the designs of SHGs. This lack of detail
complicates the analysis of moderating effects in the meta-analysis tremendously.
The lack of attention for causal identification in the qualitative studies and the lack
of detail about the program in the quantitative studies complicate the integration of
quantitative and qualitative studies.
Based on the findings discussed above and the relatively small number of
quantitative studies that adequately account for selection bias, we argue that,
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although we are able to determine the average pooled effect of self-help groups on
empowerment across studies with reasonable precision, there are not yet enough
rigorous quantitative studies about self-help groups that present sufficient details
about the program design to answer second-generation questions with respect to
their effectiveness, such as whether self-help groups with a specific training
component are more effective than self-help groups that merely provide financial
services and group-support. It is still unclear how to organize self-help groups to
achieve maximum impacts on women’s empowerment. For example, we would need
more evidence to understand what types of training result in women’s
empowerment.
Nevertheless, for other findings, the strength of an integrated mixed-methods review
is clearly visible. For example, the quantitative evidence suggests positive effects on
various dimensions of empowerment, which the qualitative evidence reinforces with
its emphasis on the mechanisms of the underlying the positive effects. Here, the
quantitative evidence addresses the attribution question and shows that women’s
self-help groups have positive causal effects on women’s empowerment. The
qualitative evidence presents a more nuanced understanding of how these
empowerment processes might work. First, women’s perspectives suggest that
economic empowerment may be stimulated by giving women the opportunity to
handle money. Second, women’s perspectives indicate that social empowerment
may be stimulated by improvements in social networks, community respect, and
solidarity among women self-help group members. Third, the integration of the
quantitative and qualitative evidence suggests that, although women’s self-help
groups may stimulate political empowerment, changing the status of women in
society is not the main goal of women SHG members. Fourth, women experiences
suggest women SHG members are able to speak freely in front of others in contrast
to before their membership. These four mechanisms indicate that the original theory
of change may miss several intermediate steps in the causal chain. Figure 4.18
depicts a revised theory of change based on our findings.
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Figure 4.18: Revised theory of change
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5 Discussion
5.1 SUMMARY OF MAIN RESULTS
Our results suggest that self-help groups can have positive effects on various
dimensions of women’s empowerment. We found positive effects, ranging from
0.06–0.41 standard deviations, on economic, and political empowerment, as well as
women’s family-size decision making power and mobility, which can both be
included under social empowerment. However, we did not find evidence for positive
effects on psychological empowerment. These findings are based on the results of
RCTs and higher quality quasi-experimental studies.
The qualitative synthesis we presented also indicates that women’s perspectives
suggest that self-help groups contribute positively to their empowerment. The
qualitative results showed a more nuanced understanding of how women experience
the phenomenon of empowerment after they enter self-help groups. Women’s
experiences suggested that the positive effects of self-help groups on economic,
social, and political, empowerment may run through the channels of familiarity with
handling money and independence in financial decision making, solidarity,
improved social networks, and respect from the household and other community
members. In contrast to the quantitative evidence, the qualitative synthesis of
women’s perceptions indicate that SHGs may contribute to psychological
empowerment.
Our synthesis of women’s experiences in SHGs also suggests that while participation
in self-help group can initially create tension within households, especially between
husbands and wives, in the long term participation in SHGs does not contribute to
domestic violence. This finding is in alignment with the lack of evidence for a
statistically significant effect of SHGs on the likelihood of domestic violence in our
meta-analysis.
The findings on community push-back were mixed and may be context-specific. For
example, De Hoop et al. (2014) demonstrated that push-back from conservative
community members in India resulted in negative consequences for happiness or
subjective well-being for women SHG members, but only in communities with
relatively conservative gender norms. Women’s perspectives from the qualitative
synthesis also present evidence for occasional backlash from other community
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members. However, our synthesis also suggests that backlash is more prevalent in
contexts where SHGs are less well established. Although we have to be cautious in
interpreting this result because of the difficulty of establishing causal effects with
qualitative research, some of the quantitative studies also presented suggestive
evidence that spillovers from self-help groups may benefit the social empowerment
of women residents in the community who were not themselves members of self-
help groups. These spillovers may be more likely in settings where SHGs are more
established.
5.2 OVERALL COMPLETENESS AND APPLICABILITY OF
EVIDENCE
A secondary goal of this research was to develop a new theory of change underlying
self-help groups using a triangulation of research findings from the quantitative
meta-analysis and the qualitative narrative synthesis. As discussed in section 4, the
positive effects of self-help groups on various dimensions of women’s empowerment
indicate that the theory of change we presented at the beginning of this review is at
least valid to a certain degree but missed several important steps. A triangulation of
the quantitative and qualitative research findings further indicates that a higher
level of group-based support in the form of training might contribute more to
women’s economic empowerment than the microfinance services of self-help
groups. However, we have to be careful in interpreting this result because of the lack
of details quantitative studies present about the contents of training in SHGs.
More fundamentally, our research findings also indicate that the original theory of
change we presented was not complete. First, the theory of change only started at
the stage where women already participate in self-help groups. But, as White (2014)
argued, many development programs fail because of the low level of participation in
the program or, in other words, the take-up of the program is too low. Our
qualitative synthesis suggested that women perceive low participation of the poorest
of the poor in self-help group programs. So self-help group programs might
currently bring more benefits to a group whose members are not the poorest of the
poor. Therefore, we propose to start the theory of change with potential
encouragements that might be necessary to stimulate the poorest of the poor to
participate in self-help groups. These incentives might be either financial, for
example, by offering the opportunity to participate with no savings requirements, or
nonfinancial, for example, by stimulating the husbands or mothers-in-law of the
poorest of the poor to let their spouses and daughters-in-law participate in self-help
group programs.
In addition, our qualitative synthesis suggests that various intermediate outcomes
were missing from the original theory of change. First, women’s perspectives
indicate that SHGs may only contribute to psychological empowerment if women
are able to gain a public voice. Second, women’s perspectives indicate that women
may first need to gain the skill to handle money before women can achieve economic
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empowerment. Third, women’s perspectives suggest that SHGs contribute to social
empowerment after women gain respect from community members, which
potentially increases the quality of their social networks and improves solidarity
among group members. Fourth, women’s perspectives about their participation in
SHGs suggested that they need to go through various stages of political
empowerment, of which only some can be achieved with SHG membership. Women
SHG members’ perceptions from the qualitative research suggest that women self-
help group members only achieved the first stage. In this stage, women became
knowledgeable about their rights, but they did not directly challenge women’s status
in society. Importantly, however, none of the quantitative studies was able to
directly test these mechanisms. Thus, we need to remain careful in the
interpretation of these results from the qualitative analysis because of the potential
of various biases.
With respect to adverse outcomes, our integration of the quantitative and qualitative
synthesis suggests that participation in women’s self-help groups is not likely to
have strong adverse effects on domestic violence. We did not find evidence for
positive effects of SHGs on the likelihood of domestic violence. Furthermore,
women’s perspectives indicate that even if SHGs contribute to domestic violence,
this adverse consequence is likely to disappear in the long term.
Finally, the strong heterogeneity in the impact estimates on social empowerment
and the wide range of potential mechanisms from the qualitative research indicate
the theory of change needs to represent the social and political context within which
women are making decisions. For example, women might not choose to become
autonomous because this might result in community disapproval. Or women might
not choose to participate in a self-help group because then they would no longer
have the time required to conduct agricultural labor. We argue that the
considerations discussed previously need to be reflected in the theory of change by
adding assumptions along the causal chain from inputs to intermediate to final
outcomes. First, women may need to support in introducing the purpose of SHG
participation to other household members before they start participating in self-help
groups. Second, women need to show demand for the financial and nonfinancial
services the self-help group provides, and have sufficient time to participate in the
activities of the self-help group.
5.3 QUALITY OF THE EVIDENCE
The findings of every systematic review depend on the quality of the primary studies
on which the review relies. In our case, we believe both the quantitative and the
qualitative studies on women’s self-help groups suffered from substantial limitations
with respect to their quality. However, we also believe that our risk of bias
assessment for the quantitative research allowed us to distinguish clearly between
the findings of studies with high, medium, and low risk of bias. The meta-analysis
indeed showed that studies with a high risk of selection-bias were likely to present
105 The Campbell Collaboration | www.campbellcollaboration.org
biased estimates on the impact of women’s self-help groups on women’s
empowerment. For this reason, we were only able to present a meta-analysis for a
small number of studies. We were not able to show strong evidence for
heterogeneous effects in a large sample of studies; even though; our analysis
presented clear indications for strong heterogeneities in the effect sizes.
In addition, we were not able to present a convincing meta-analysis for the effects of
self-help groups on women’s psychological and political empowerment.
Nonetheless, the results of the meta-analysis for studies with high and medium risks
of selection bias presented important evidence of the effects of self-help groups on
women’s empowerment.
The qualitative evidence also presented important findings with respect to the
possible mediators of the effectiveness of women’s self-help groups. But the lack of
qualitative studies that report the empowering experiences of women in self-help
groups directly from women’s narratives limited our ability to more fully understand
such mediators. Furthermore, several of the qualitative studies suffered from a
medium risk of bias.
5.4 LIMITATIONS AND POTENTIAL BIASES IN THE
REVIEW PROCESS
The limitations of this review are specific to the two types of analyses and appeared
in the synthesis process to triangulate the quantitative and qualitative results. In
particular, we were not able to triangulate all the research findings with respect to
the qualitative synthesis in the quantitative meta-analysis. This was partly because
of the small number of studies in the quantitative meta-analysis that could be
considered rigorous. More importantly, however, the majority of the potential
moderators in the qualitative research were not reported in the quantitative research
or insufficient details were provided. Hence, we were not able to estimate the
moderating effect of potential moderators identified in the qualitative research.
Furthermore, although we were able to assess the moderating effect of training in
the quantitative analysis, suggesting that training has positive effects on
empowerment, both the quantitative and the qualitative studies did not present
sufficient details about the contents of training in SHGs. Thus, we need to remain
very careful in the interpretation of this result.
5.4.1 Limitations of quantitative data analysis
Publication bias: The results of our meta-analysis may be vulnerable to publication
bias. We tested for the presence of potential publication bias by reporting funnel
plots for the effects on women’s social (women’s family-size decision-making power
and mobility) and economic empowerment and reporting the results of the Egger
test. From these funnel plots, we concluded that there might be scope for publication
bias with respect to the impact estimates of women’s self-help groups on women’s
106 The Campbell Collaboration | www.campbellcollaboration.org
economic and social empowerment. However, the Egger test did not show formal
statistical evidence for publication bias in the impact estimates of women’s self-help
groups on women’s economic or social empowerment. So based on the funnel plots,
we can merely say there was potential for publication bias in the studies that focused
on women’s economic or social empowerment.
In addition, the results of our both our quantitative and qualitative synthesis are
heavily based on studies from India and Bangladesh. The external validity of the
review may thus be limited to the context of South Asia. At the same time our results
may also be most relevant for the context of South Asia because self-help groups are
a more popular intervention to stimulate women’s empowerment in this region than
in other regions of the world.
Missing information: Unfortunately, in this review, we were not able to distinguish
among the effects of different self-help group models because studies often did not
report sufficient information about the specific model on which they focused. And a
wide variety of self-help group models exist across regions. The Indian model was
quite different from the model in Bangladesh, and even within India, a wide range of
different self-help group models exist. The differences among self-help group
models in South Asia and the rest of the world are even larger.
The results of our quantitative synthesis might also be biased due to the exclusion of
studies from the meta-analysis for which we were not able to estimate effect sizes.
We believe this risk was minimal, however. In general, the findings of the studies we
were not able to include in our meta-analysis were consistent with the findings of
studies that we were able to include in our meta-analysis. Further, the study findings
that were not in line with the findings of the meta-analysis were generally based on
studies with a high risk of selection bias. Data analysis: Unfortunately, the number
of studies with only a low or medium risk of selection bias was limited. Therefore, we
were able to convincingly demonstrate the effectiveness of women’s self-help groups
with respect to social and economic empowerment only. For the effects on
psychological and political empowerment we had to mostly rely on a narrative
synthesis. In addition, we were not able to convincingly demonstrate the effects of
women’s self-help groups on women’s economic and social empowerment for
subgroups using a meta-analysis. For this purpose, we again had to rely on a
narrative synthesis. Finally, many studies used different outcome measures to
measure the same empowerment domain. Thus, the outcome variables in our meta-
analysis may not always measure the same construct. We, however, took this
concern seriously as shown by our decision to separately analyze impact estimates
on women’s family-size decision-making power and women’s mobility after our
evidence suggested that these two empowerment components cannot be considered
part of the same construct. Notwithstanding these limitations, we believe our
systematic review presents important evidence with respect to the pooled impact
estimate and the heterogeneities in that pooled impact estimate of women’s self-help
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groups on women’s economic, social, and less convincingly political and
psychological empowerment.
5.4.2 Limitations of qualitative data analysis
Searches: Given the large scope of this review, it is possible that we have missed
some articles that may have been relevant. We made a concerted attempt to find all
relevant qualitative studies. But we noticed that fewer qualitative evaluations exist
and even fewer make it into peer-reviewed publications. Therefore, our search
strategy also emphasized the gray literature, including dissertations and
unpublished reports. One of the most comprehensive qualitative studies that we
included was a dissertation. The value of this piece was further emphasized by the
lack of any page limitations, and therefore, the author could include full quotations
from female SHG participants and cover many different themes. What appeared in
the peer-reviewed literature was less comprehensive, with fewer quotations and less-
developed theoretical frameworks. It was unclear if this finding was representative
of the lack of strong qualitative studies altogether or if there was a bias in what ends
up being published versus the type of research actually conducted. Finally, because
of the interdisciplinary nature of the review questions spanning public health,
psychology, economics, law, and human rights, it is possible that relevant
psychology reports or legal documentation did not meet the inclusion criteria of this
review.
Underreporting of adverse outcomes: The included qualitative studies intended to
examine changes in empowerment outcomes as stated in their research questions.
As a result, qualitative researchers spoke with group members who were willing to
talk about their experiences and not with women who did not want to be
interviewed, who dropped-out or who did not join SHGs. In addition, researchers
did not talk to men or other community members who may have different
perspectives about the SHGs. As a result, it may be possible that adverse effects of
SHGs were underreported in the qualitative research.
Missing information: Although the authors conducted a thorough quality
assessment of each study, there are concerns that descriptions of important
methodological processes were missing from many of the qualitative studies. For
example, although the data analysis of a study might have appeared rigorous as
judged by the results presented, the description of the process of analysis was weak
in most studies. In addition, the discussion about the researcher’s relationship with
the study participants and ethical considerations were either unreported or not
examined. These are important parts of any qualitative research and should also be
reported in any dissemination of the findings. The risk of bias summary table (4.2)
offers a way for readers to assess completeness for themselves.
Data analysis: The meta-ethnographic process attempts to use the included studies
much like one would use transcripts in a qualitative analysis. The quality and
108 The Campbell Collaboration | www.campbellcollaboration.org
completeness of the transcripts affects the analysis process and the results. The
direct quotations from women in the studies were as close to the raw data as we
could get—similar to having access to a dataset in quantitative research.
Unfortunately, some studies provided more direct quotations from women than
other studies, and the analysis was therefore biased toward studies that included
more quotations.
5.4.3 Limitations of the synthesis process
The theory of an integrated mixed-method review is that the two parts of the
analysis can inform each other during the analysis process and not just in the
conclusions. Therefore, the researchers working on the two parts of the study spent
time during the data extraction and analysis phase discussing findings but there
were limitations in how much the exchange of information could impact each
analysis. For example, very few concepts that emerged from the qualitative studies
could be used in the subgroup analysis of the quantitative studies because of missing
data.
We believe that integrated mixed-method reviews that include both quantitative and
qualitative research have potential. However, to optimize the learning from
integrated mixed-methods reviews, it is important that quantitative researchers
integrate the findings of qualitative researchers in their research design and vice
versa. Hence, maximizing the potential of integrated mixed-methods reviews would
require a more interdisciplinary attitude from both quantitative and qualitative
researchers.
5.5 AGREEMENTS AND DISAGREEMENTS WITH OTHER
STUDIES OR REVIEWS
The systematic review found positive significant impacts of self-help groups on
empowerment, whereas the systematic reviews that focused on microcredit and
microsavings (e.g. Stewart et al., 2012; Vaessen et al., 2014) only found limited
evidence for positive effects on economic outcomes. In addition, our quantitative
synthesis suggested that self-help group interventions that include a training
component may have stronger effects on women’s empowerment, particularly
economic empowerment and women’s family-size decision making power, than self-
help groups that do not contain a training component. So although our results
presented more positive findings than other systematic reviews with an emphasis on
microfinance, we do not believe the results from the different reviews are necessarily
contrasting. However, we need to remain very careful in the interpretation of this
result because the quantitative studies neither presented sufficient about the
training components of SHGs nor about other details of the trainings.
109 The Campbell Collaboration | www.campbellcollaboration.org
6 Authors’ conclusions
6.1 IMPLICATIONS FOR PRACTICE AND POLICY
Our review highlights several important implications for practice and policy related
to the rollout and potential impact of SHGs. First, our quantitative evidence
suggested positive effects on women’s empowerment indicating that self-help groups
have the potential to strengthen development outcomes. These findings have
important implications for program designers and managers. Thorough program
planning and implementation is essential to ensure an optimal number of
participants meet frequently. In addition, staff and institutions may consider
structures that will ensure the same staff and institutions are accountable to their
clients.
The greatest quantitative impacts were found among SHGs where health education,
life skills training, and/or other types of information were shared and supported.
The additional benefits accrued via group training, such as group sharing, learning,
and support. Furthermore, it is important for programs to consider that SHGs offer
an important venue to deliver additional services and training. SHGs that are
facilitated externally are also more likely to have the resources to provide additional
components, such as training. The finding on training might also reflect the success
of programs in which more holistic programming is provided as indicated by the
qualitative research. However, unfortunately, the quantitative studies do not present
details about the contents of the training. Thus, we have to remain careful in
interpreting this finding.
One area that has particularly important implications for programs and policy is the
qualitative finding that women SHG members perceive low participation of the
poorest of the poor in self-help groups. In part, this might be because the poorest of
the poor are too financially and/or socially constrained to join self-help groups or to
benefit from the financial services most often provided through self-help groups. But
other barriers such as class or caste discrimination might also be occurring. Poorer
or marginalized women may not feel accepted by groups that are made up of
wealthier or better connected community members. It is important for program and
policy makers, as well as researchers, to identify ways to build in support and reduce
barriers for individual women who want to participate in such groups but who do
not have the financial resources or freedoms to join. One enhancement that we have
made based on the findings from this review is to start the theory of change related
110 The Campbell Collaboration | www.campbellcollaboration.org
to SHGs with encouragements to stimulate the poorest of the poor to participate in
self-help groups. These incentives could be financial, for example, by giving the
poorest of the poor the opportunity to participate without a savings requirements, or
nonfinancial, for example, by stimulating the husbands or mothers-in-law of the
poorest of the poor to let their spouses and daughters-in-law participate in self-help
group programs or conducting outreach activities to marginalized groups.
It is important to note that although SHGs overall showed positive impacts, both the
quantitative and qualitative evidence showed there was much heterogeneity across
program designs and the effectiveness of programs. This finding indicates that
context matters. The types of specific program components, and the likely impacts,
depend on the overarching social, cultural, political, and economic context from a
national level down to a very local level. As new programs are implemented in
different contexts, and as more nascent groups become more established, it is
critical that program designs are tailored to the local settings in ways that allow
them to evolve over time. Such consideration may include conducting community
readiness activities, performing more comprehensive outreach to marginalized
groups even within small communities, and included some form of advocacy
training so that women might address change beyond the individual level and
towards overcoming structural barriers to empowerment. This review has shown
that one-size does not fit all, and while there is a need to take best practices across
programs for implementation, this needs to be done in a flexible way to adapt
programs most successfully for the greatest impact in women’s lives.
6.2 IMPLICATIONS FOR RESEARCH
This review has several implications for research. First, the synthesis of the
quantitative evidence suggests there is a need for more rigorous quantitative studies
that can correct for selection bias, spillovers, and the difficulties of measuring
empowerment. The quantitative synthesis indicated that studies that did not
adequately account for selection bias overestimated the impact of self-help groups
on empowerment. Furthermore, the qualitative synthesis suggested that the current
measurements of empowerment in the quantitative studies might not reliably
capture all dimensions of empowerment. Whereas the quantitative measures are
useful in understanding certain aspects of the impact of self-help groups on
empowerment, the qualitative studies show us more nuanced ideas about how to
measure the lived experience of empowerment. In both cases (quantitative and
qualitative studies), researchers need to describe more fully the various components
of the interventions/programs being studied, so outcomes and findings can be
understood and interpreted against the specifics of the program components.
Greater detail in the description of the program design will help in determining
moderating factors in the design of SHGs. In addition, future research could draw on
mixed-method strategies to develop and test new rigorous measures of
empowerment.
111 The Campbell Collaboration | www.campbellcollaboration.org
Second, there is a need for more research focused on examining the impact of
economic self-help groups on women’s empowerment using meditator and
moderator analysis to further understand the pathways or mechanisms through
which SHGs impact empowerment. In addition, there may be other pathways not
examined in this review that lead to empowerment that can be rigorously measured
(or measure development embarked on) for inclusion in future studies that examine
the impact of women’s SHGs on empowerment. Potential mediators/moderators of
interest include indicators of mental health, relationship power, community-level
respect, social capital, and social solidarity. In addition, other important mediators
may include an understanding of whether women who participate in SHGs have
male partners who experience shifts in gender-related attitudes in the direction of
more gender equality as measured by the gender equitable man scale (Pulerwitz et
al., 2008). Future research can examine if and how men’s attitudinal shifts impact,
positively or negatively, women’s empowerment. Furthermore, the effects of these
complex interventions take time to influence both mediators and outcomes; thus,
longer follow-up periods are needed in future research to understand fully both the
long-term impacts of SHGs and the factors that support the maintenance of
empowerment.
Because women’s self-help programs are implemented across many different regions
of the world, it is also critical for researchers to not assume that an intervention that
works in one place should be replicated elsewhere. In short, as alluded to, nuanced
modifications of programs and sensitivity to local cultural norms are needed in
future program design and in the evaluation of program impacts.
Another interesting dimension of our review, where we were not able to make
definitive conclusions, is the effectiveness of SHGs that integrate components other
than economic ones (skills-building, reduced family size, reproductive health) and
whether these “integrated programs” result in more social, psychological, political,
or economic empowerment for women.
112 The Campbell Collaboration | www.campbellcollaboration.org
7 Acknowledgments
We would like to thank 3ie for funding this study. We would also like to
acknowledge our advisory group including Reema Nanavaty of SEWA and Shahid
Vaziralli from the Center for Microfinance, search strategist Tara Horvath and
research assistants, Tra Truong, Julie Weiland and Keely Molina Johnson.
Furthermore, we would like to thank Hugh Waddington and John Eyers for very
thoughtful comments and suggestions on earlier versions of this review and David
Wilson for support in the calculation of the effect sizes.
113 The Campbell Collaboration | www.campbellcollaboration.org
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132 The Campbell Collaboration | www.campbellcollaboration.org
9 Appendices
APPENDIX 1: DATA EXTRACTION FORM
Study Data Extraction/Coding
Study ID (sid):
Coders Initials (coderid):
Date Coded (date):
Author(s) (author):
Funder (funder):
Publication date (pubdate):
Country (country):
Start date of study (startdate):
Start date of study (enddate):
Publication type (pubtype): (1) Book, (2) peer-reviewed journal, (3) book chapter, (4)
dissertation/thesis, (5) unpublished report
SHG Data Extraction/Coding
Study ID (sid):
Coders initials (coderid):
Date coded (date):
Name of self-help group (shgname):
Location of group (glocale):
Region (gregion):
Target population (targetpop):
Type of group (gtype): (1) economic, (2) livelihood, (3) other
Number of intervention components (numcomp):
Type of component: (1) credit, (2) savings, (3) loans, (4) insurance (5) capacity
building:
Type of component 1 (comp1)
Type of component 2 (comp2)
Type of component 3 (comp3)
Type of component 4 (comp4)
Type of component 5 (comp5)
Group origin (origin): (1) community-based, (2) organization-based (3) research-
based
Study design (design):
133 The Campbell Collaboration | www.campbellcollaboration.org
Nature of comparison group (compgroup):
Sample size (sampsize):
Type of sampling (samptype): (1) random (2) purposive (3) convenience (4) cannot
tell
Did researchers assess baseline differences? (basediff)
If yes, were there differences? (difftype) (1) no (2) minor (3) major (4) cannot tell
Outcome Extraction/Coding
Study ID (sid):
Coders initials (coderid):
Date coded (date):
Outcome category (outcat): (1) economic (2) political (3) social (4) psychological
Outcome name (outname):
Type of information (outtype): (1) quantitative (2) qualitative
Source of information (outsource): (1) survey (2) records (3) interviews (4) focus
groups
Measure/Indicator of outcome (measure):
Were there any differences in measurement of this outcome between the group
participants and the comparison? (1) yes (2) no (3) cannot tell
Effect Size Extraction/Coding
Study ID (sid):
Coders initials (coderid):
Date coded (date):
Outcome category (outcat): (1) economic (2) political (3) social (4) psychological
Outcome name (outname):
Direction of effect (esdir): (1) effect favors self-help group (2) effect favors comparison
(3) effect favors neither (4) cannot tell
Effect is statistically significant (essig)?: (1) yes (2) no (3) cannot tell
SHG sample size (shgss):
Comparison sample size (compss):
For continuous measures:
SHG group mean (txmean):
Comparison group mean (compmean):
Are means reported above adjusted? (meanadj): (1) yes (2) no
SHG group standard deviation (txsd):
Comparison group standard deviation (compsd):
SHG group standard error (txse):
Comparison group standard error (compse):
t-value from an independent t-test (est)
134 The Campbell Collaboration | www.campbellcollaboration.org
For dichotomous measures:
SHG group number of participants who experienced a change (txnum):
Comparison group number of participants who experienced a change (compnum):
SHG group proportion of participants who experienced a change (txpro):
Comparison group proportion of participants who experienced a change (comppro):
Are the proportions above adjusted for pretest variables? (proadj): (1) yes (2) no
Logged odds-ratio (eslgodd):
Standard error of logged odds-ratio (eslgoddse):
Logged odds-ratio adjusted? (e.g., from a logistic regression analysis with other
independent variables) (1=yes; 0=no)
Chi-square value with df = 1 (2 by 2 contingency table) (eschi):
Correlation coefficient (esphi):
For Hand Calculated Data:
Hand calculated d-type effect size (eshand1)
Hand calculated standard error of the d-type effect size (eshand2)
Hand calculated odds-ratio effect size (eshand3)
Hand calculated odds-ratio standard error (eshand4)
Intermediate outcomes or themes (knowledge, skills):
For qualitative data:
Participants views (views):
Themes (mtheme):
Subthemes (stheme):
Sources: Wilson et al.
APPENDIX 2: FULL SEARCH STRATEGY
Search Query Items
found
#5 Search ((#1) AND #2) AND #3 Filters: Publication
date from 1980/01/01 to 2012/12/31
1741
135 The Campbell Collaboration | www.campbellcollaboration.org
#4 Search ((#1) AND #2) AND #3 1811
#3 Search “women’s self-help”[tiab] OR “women’s
cooperative*”[tiab] OR “self-help group*”[tiab] OR “self
help group*”[tiab] OR “support group*”[tiab] OR “lending
group*”[tiab] OR “advocacy group*”[tiab] OR “micro
finance”[tiab] OR “micro credit”[tiab] OR
“microfinance”[tiab] OR “microcredit”[tiab] OR “income
generation group*”[tiab] OR “microenterprise
group*”[tiab] OR sangha[tiab] OR "Self-Help
Groups"[Mesh] OR (women*[tiab] AND (financ*[tiab] OR
economic*[tiab])) OR (“Women”[Mesh] AND (“Financing,
Organized”[Mesh] OR “Economics”[Mesh]))
29946
#2 Search “women’s empowerment”[tiab] OR “empower*”
[tiab] OR “girl’s empowerment”[tiab] OR
“empowering”[tiab] OR “power”[tiab] OR “control”[tiab]
OR “Power (Psychology)”[Mesh]
1743835
#1 Search (“developing country”[tiab] OR “developing
countries”[tiab] OR “developing nation”[tiab] OR
“developing nations”[tiab] OR “developing
population”[tiab] OR “developing populations”[tiab] OR
“developing world”[tiab] OR “less developed country”[tiab]
OR “less developed countries”[tiab] OR “less developed
nation”[tiab] OR “less developed nations”[tiab] OR “less
developed population”[tiab] OR “less developed
populations”[tiab] OR “less developed world”[tiab] OR
“lesser developed country”[tiab] OR “lesser developed
countries”[tiab] OR “lesser developed nation”[tiab] OR
“lesser developed nations”[tiab] OR “lesser developed
population”[tiab] OR “lesser developed populations”[tiab]
OR “lesser developed world”[tiab] OR “under developed
country”[tiab] OR “under developed countries”[tiab] OR
“under developed nation”[tiab] OR “under developed
nations”[tiab] OR “under developed population”[tiab] OR
“under developed populations”[tiab] OR “under developed
world”[tiab] OR “underdeveloped country”[tiab] OR
“underdeveloped countries”[tiab] OR “underdeveloped
nation”[tiab] OR “underdeveloped nations”[tiab] OR
“underdeveloped population”[tiab] OR “underdeveloped
populations”[tiab] OR “underdeveloped world”[tiab] OR
“middle income country”[tiab] OR “middle income
countries”[tiab] OR “middle income nation”[tiab] OR
“middle income nations”[tiab] OR “middle income
population”[tiab] OR “middle income populations”[tiab]
OR “low income country”[tiab] OR “low income
countries”[tiab] OR “low income nation”[tiab] OR “low
income nations”[tiab] OR “low income population”[tiab]
OR “low income populations”[tiab] OR “lower income
country”[tiab] OR “lower income countries”[tiab] OR
1139069
136 The Campbell Collaboration | www.campbellcollaboration.org
“lower income nation”[tiab] OR “lower income
nations”[tiab] OR “lower income population”[tiab] OR
“lower income populations”[tiab] OR “underserved
country”[tiab] OR “underserved countries”[tiab] OR
“underserved nation”[tiab] OR “underserved nations”[tiab]
OR “underserved population”[tiab] OR “underserved
populations”[tiab] OR “underserved world”[tiab] OR
“under served country”[tiab] OR “under served
countries”[tiab] OR “under served nation”[tiab] OR “under
served nations”[tiab] OR “under served population”[tiab]
OR “under served populations”[tiab] OR “under served
world”[tiab] OR “deprived country”[tiab] OR “deprived
countries”[tiab] OR “deprived nation”[tiab] OR “deprived
nations”[tiab] OR “deprived population”[tiab] OR
“deprived populations”[tiab] OR “deprived world”[tiab] OR
“poor country”[tiab] OR “poor countries”[tiab] OR “poor
nation”[tiab] OR “poor nations”[tiab] OR “poor
population”[tiab] OR “poor populations”[tiab] OR “poor
world”[tiab] OR “poorer country”[tiab] OR “poorer
countries”[tiab] OR “poorer nation”[tiab] OR “poorer
nations”[tiab] OR “poorer population”[tiab] OR “poorer
populations”[tiab] OR “poorer world”[tiab] OR “developing
economy”[tiab] OR “developing economies”[tiab] OR “less
developed economy”[tiab] OR “less developed
economies”[tiab] OR “lesser developed economy”[tiab] OR
“lesser developed economies”[tiab] OR “under developed
economy”[tiab] OR “under developed economies”[tiab] OR
“underdeveloped economy”[tiab] OR “underdeveloped
economies”[tiab] OR “middle income economy”[tiab] OR
“middle income economies”[tiab] OR “low income
economy”[tiab] OR “low income economies”[tiab] OR
“lower income economy”[tiab] OR “lower income
economies”[tiab] OR “low gdp”[tiab] OR “low gnp”[tiab] OR
“low gross domestic”[tiab] OR “low gross national”[tiab] OR
“lower gdp”[tiab] OR “lower gnp”[tiab] OR “lower gross
domestic”[tiab] OR “lower gross national”[tiab] OR
lmic[tiab] OR lmics[tiab] OR “third world”[tiab] OR “lami
country”[tiab] OR “lami countries”[tiab] OR “transitional
country”[tiab] OR “transitional countries”[tiab] OR
“resource-limited”[tiab] OR “resource-constrained”[tiab])
OR (Africa[tiab] OR Asia[tiab] OR Caribbean[tiab] OR West
Indies[tiab] OR South America[tiab] OR Latin
America[tiab] OR Central America[tiab] OR
Afghanistan[tiab] OR Albania[tiab] OR Algeria[tiab] OR
Angola[tiab] OR Antigua[tiab] OR Barbuda[tiab] OR
Argentina[tiab] OR Armenia[tiab] OR Armenian[tiab] OR
Aruba[tiab] OR Azerbaijan[tiab] OR Bahrain[tiab] OR
Bangladesh[tiab] OR Barbados[tiab] OR Benin[tiab] OR
Byelarus[tiab] OR Byelorussian[tiab] OR Belarus[tiab] OR
Belorussian[tiab] OR Belorussia[tiab] OR Belize[tiab] OR
137 The Campbell Collaboration | www.campbellcollaboration.org
Bhutan[tiab] OR Bolivia[tiab] OR Bosnia[tiab] OR
Herzegovina[tiab] OR Hercegovina[tiab] OR
Botswana[tiab] OR Brasil[tiab] OR Brazil[tiab] OR
Bulgaria[tiab] OR Burkina Faso[tiab] OR Burkina
Fasso[tiab] OR Upper Volta[tiab] OR Burundi[tiab] OR
Urundi[tiab] OR Cambodia[tiab] OR Khmer Republic[tiab]
OR Kampuchea[tiab] OR Cameroon[tiab] OR
Cameroons[tiab] OR Cameron[tiab] OR Cape Verde[tiab]
OR Central African Republic[tiab] OR Chad[tiab] OR
Chile[tiab] OR China[tiab] OR Colombia[tiab] OR
Comoros[tiab] OR Comoro Islands[tiab] OR Comores[tiab]
OR Mayotte[tiab] OR Congo[tiab] OR Zaire[tiab] OR Costa
Rica[tiab] OR Cote d'Ivoire[tiab] OR Ivory Coast[tiab] OR
Croatia[tiab] OR Cuba[tiab] OR Cyprus[tiab] OR
Czechoslovakia[tiab] OR Czech Republic[tiab] OR
Slovakia[tiab] OR Slovak Republic[tiab] OR Djibouti[tiab]
OR French Somaliland[tiab] OR Dominica[tiab] OR
Dominican Republic[tiab] OR East Timor[tiab] OR Timor
Leste[tiab] OR Ecuador[tiab] OR Egypt[tiab] OR United
Arab Republic[tiab] OR El Salvador[tiab] OR Eritrea[tiab]
OR Estonia[tiab] OR Ethiopia[tiab] OR Fiji[tiab] OR
Gabon[tiab] OR Gabonese Republic[tiab] OR Gambia[tiab]
OR Gaza[tiab] OR Georgia Republic[tiab] OR Georgian
Republic[tiab] OR Ghana[tiab] OR Gold Coast[tiab] OR
Greece[tiab] OR Grenada[tiab] OR Guatemala[tiab] OR
Guinea[tiab] OR Guam[tiab] OR Guiana[tiab] OR
Guyana[tiab] OR Haiti[tiab] OR Honduras[tiab] OR
Hungary[tiab] OR India[tiab] OR Maldives[tiab] OR
Indonesia[tiab] OR Iran[tiab] OR Iraq[tiab] OR Isle of
Man[tiab] OR Jamaica[tiab] OR Jordan[tiab] OR
Kazakhstan[tiab] OR Kazakh[tiab] OR Kenya[tiab] OR
Kiribati[tiab] OR Korea[tiab] OR Kosovo[tiab] OR
Kyrgyzstan[tiab] OR Kirghizia[tiab] OR Kyrgyz
Republic[tiab] OR Kirghiz[tiab] OR Kirgizstan[tiab] OR
“Lao PDR”[tiab] OR Laos[tiab] OR Latvia[tiab] OR
Lebanon[tiab] OR Lesotho[tiab] OR Basutoland[tiab] OR
Liberia[tiab] OR Libya[tiab] OR Lithuania[tiab] OR
Macedonia[tiab] OR Madagascar[tiab] OR Malagasy
Republic[tiab] OR Malaysia[tiab] OR Malaya[tiab] OR
Malay[tiab] OR Sabah[tiab] OR Sarawak[tiab] OR
Malawi[tiab] OR Nyasaland[tiab] OR Mali[tiab] OR
Malta[tiab] OR Marshall Islands[tiab] OR Mauritania[tiab]
OR Mauritius[tiab] OR Mexico[tiab] OR Micronesia[tiab]
OR Middle East[tiab] OR Moldova[tiab] OR Moldovia[tiab]
OR Moldovian[tiab] OR Mongolia[tiab] OR
Montenegro[tiab] OR Morocco[tiab] OR Ifni[tiab] OR
Mozambique[tiab] OR Myanmar[tiab] OR Myanma[tiab]
OR Burma[tiab] OR Namibia[tiab] OR Nepal[tiab] OR
Netherlands Antilles[tiab] OR New Caledonia[tiab] OR
Nicaragua[tiab] OR Niger[tiab] OR Nigeria[tiab] OR
138 The Campbell Collaboration | www.campbellcollaboration.org
Northern Mariana Islands[tiab] OR Oman[tiab] OR
Muscat[tiab] OR Pakistan[tiab] OR Palau[tiab] OR
Palestine[tiab] OR Panama[tiab] OR Paraguay[tiab] OR
Peru[tiab] OR Philippines[tiab] OR Philipines[tiab] OR
Phillipines[tiab] OR Phillippines[tiab] OR Poland[tiab] OR
Portugal[tiab] OR Puerto Rico[tiab] OR Romania[tiab] OR
Rumania[tiab] OR Roumania[tiab] OR Russia[tiab] OR
Russian[tiab] OR Rwanda[tiab] OR Ruanda[tiab] OR Saint
Kitts[tiab] OR St Kitts[tiab] OR Nevis[tiab] OR Saint
Lucia[tiab] OR St Lucia[tiab] OR Saint Vincent[tiab] OR St
Vincent[tiab] OR Grenadines[tiab] OR Samoa[tiab] OR
Samoan Islands[tiab] OR Sao Tome[tiab] OR Saudi
Arabia[tiab] OR Senegal[tiab] OR Serbia[tiab] OR
Montenegro[tiab] OR Seychelles[tiab] OR Sierra
Leone[tiab] OR Slovenia[tiab] OR Sri Lanka[tiab] OR
Ceylon[tiab] OR Solomon Islands[tiab] OR Somalia[tiab]
OR Sudan[tiab] OR Suriname[tiab] OR Surinam[tiab] OR
Swaziland[tiab] OR Syria[tiab] OR Tajikistan[tiab] OR
Tadzhikistan[tiab] OR Tadjikistan[tiab] OR Tadzhik[tiab]
OR Tanzania[tiab] OR Thailand[tiab] OR Togo[tiab] OR
Togolese Republic[tiab] OR Tonga[tiab] OR Trinidad[tiab]
OR Tobago[tiab] OR Tunisia[tiab] OR Turkey[tiab] OR
Turkmenistan[tiab] OR Turkmen[tiab] OR Uganda[tiab]
OR Ukraine[tiab] OR Uruguay[tiab] OR USSR[tiab] OR
Soviet Union[tiab] OR Union of Soviet Socialist
Republics[tiab] OR Uzbekistan[tiab] OR Uzbek OR
Vanuatu[tiab] OR New Hebrides[tiab] OR Venezuela[tiab]
OR Vietnam[tiab] OR Viet Nam[tiab] OR West Bank[tiab]
OR Yemen[tiab] OR Yugoslavia[tiab] OR Zambia[tiab] OR
Zimbabwe[tiab]) OR (Developing Countries[Mesh:noexp]
OR Africa[Mesh:noexp] OR Africa, Northern[Mesh:noexp]
OR Africa South of the Sahara[Mesh:noexp] OR Africa,
Central[Mesh:noexp] OR Africa, Eastern[Mesh:noexp] OR
Africa, Southern[Mesh:noexp] OR Africa,
Western[Mesh:noexp] OR Asia[Mesh:noexp] OR Asia,
Central[Mesh:noexp] OR Asia, Southeastern[Mesh:noexp]
OR Asia, Western[Mesh:noexp] OR Caribbean
Region[Mesh:noexp] OR West Indies[Mesh:noexp] OR
South America[Mesh:noexp] OR Latin
America[Mesh:noexp] OR Central America[Mesh:noexp]
OR Afghanistan[Mesh:noexp] OR Albania[Mesh:noexp] OR
Algeria[Mesh:noexp] OR American Samoa[Mesh:noexp]
OR Angola[Mesh:noexp] OR “Antigua and
Barbuda”[Mesh:noexp] OR Argentina[Mesh:noexp] OR
Armenia[Mesh:noexp] OR Azerbaijan[Mesh:noexp] OR
Bahrain[Mesh:noexp] OR Bangladesh[Mesh:noexp] OR
Barbados[Mesh:noexp] OR Benin[Mesh:noexp] OR
Byelarus[Mesh:noexp] OR Belize[Mesh:noexp] OR
Bhutan[Mesh:noexp] OR Bolivia[Mesh:noexp] OR Bosnia-
Herzegovina[Mesh:noexp] OR Botswana[Mesh:noexp] OR
139 The Campbell Collaboration | www.campbellcollaboration.org
Brazil[Mesh:noexp] OR Bulgaria[Mesh:noexp] OR Burkina
Faso[Mesh:noexp] OR Burundi[Mesh:noexp] OR
Cambodia[Mesh:noexp] OR Cameroon[Mesh:noexp] OR
Cape Verde[Mesh:noexp] OR Central African
Republic[Mesh:noexp] OR Chad[Mesh:noexp] OR
Chile[Mesh:noexp] OR China[Mesh:noexp] OR
Colombia[Mesh:noexp] OR Comoros[Mesh:noexp] OR
Congo[Mesh:noexp] OR Costa Rica[Mesh:noexp] OR Cote
d'Ivoire[Mesh:noexp] OR Croatia[Mesh:noexp] OR
Cuba[Mesh:noexp] OR Cyprus[Mesh:noexp] OR
Czechoslovakia[Mesh:noexp] OR Czech
Republic[Mesh:noexp] OR Slovakia[Mesh:noexp] OR
Djibouti[Mesh:noexp] OR “Democratic Republic of the
Congo”[Mesh:noexp] OR Dominica[Mesh:noexp] OR
Dominican Republic[Mesh:noexp] OR East
Timor[Mesh:noexp] OR Ecuador[Mesh:noexp] OR
Egypt[Mesh:noexp] OR El Salvador[Mesh:noexp] OR
Eritrea[Mesh:noexp] OR Estonia[Mesh:noexp] OR
Ethiopia[Mesh:noexp] OR Fiji[Mesh:noexp] OR
Gabon[Mesh:noexp] OR Gambia[Mesh:noexp] OR
“Georgia (Republic)”[Mesh:noexp] OR
Ghana[Mesh:noexp] OR Greece[Mesh:noexp] OR
Grenada[Mesh:noexp] OR Guatemala[Mesh:noexp] OR
Guinea[Mesh:noexp] OR Guinea-Bissau[Mesh:noexp] OR
Guam[Mesh:noexp] OR Guyana[Mesh:noexp] OR
Haiti[Mesh:noexp] OR Honduras[Mesh:noexp] OR
Hungary[Mesh:noexp] OR India[Mesh:noexp] OR
Indonesia[Mesh:noexp] OR Iran[Mesh:noexp] OR
Iraq[Mesh:noexp] OR Jamaica[Mesh:noexp] OR
Jordan[Mesh:noexp] OR Kazakhstan[Mesh:noexp] OR
Kenya[Mesh:noexp] OR Korea[Mesh:noexp] OR
Kosovo[Mesh:noexp] OR Kyrgyzstan[Mesh:noexp] OR
Laos[Mesh:noexp] OR Latvia[Mesh:noexp] OR
Lebanon[Mesh:noexp] OR Lesotho[Mesh:noexp] OR
Liberia[Mesh:noexp] OR Libya[Mesh:noexp] OR
Lithuania[Mesh:noexp] OR Macedonia[Mesh:noexp] OR
Madagascar[Mesh:noexp] OR Malaysia[Mesh:noexp] OR
Malawi[Mesh:noexp] OR Mali[Mesh:noexp] OR
Malta[Mesh:noexp] OR Mauritania[Mesh:noexp] OR
Mauritius[Mesh:noexp] OR Mexico[Mesh:noexp] OR
Micronesia[Mesh:noexp] OR Middle East[Mesh:noexp] OR
Moldova[Mesh:noexp] OR Mongolia[Mesh:noexp] OR
Montenegro[Mesh:noexp] OR Morocco[Mesh:noexp] OR
Mozambique[Mesh:noexp] OR Myanmar[Mesh:noexp] OR
Namibia[Mesh:noexp] OR Nepal[Mesh:noexp] OR
Netherlands Antilles[Mesh:noexp] OR New
Caledonia[Mesh:noexp] OR Nicaragua[Mesh:noexp] OR
Niger[Mesh:noexp] OR Nigeria[Mesh:noexp] OR
Oman[Mesh:noexp] OR Pakistan[Mesh:noexp] OR
Palau[Mesh:noexp] OR Panama[Mesh:noexp] OR Papua
140 The Campbell Collaboration | www.campbellcollaboration.org
New Guinea[Mesh:noexp] OR Paraguay[Mesh:noexp] OR
Peru[Mesh:noexp] OR Philippines[Mesh:noexp] OR
Poland[Mesh:noexp] OR Portugal[Mesh:noexp] OR Puerto
Rico[Mesh:noexp] OR Romania[Mesh:noexp] OR
Russia[Mesh:noexp] OR Rwanda[Mesh:noexp] OR “Saint
Kitts and Nevis”[Mesh:noexp] OR Saint Lucia[Mesh:noexp]
OR “Saint Vincent and the Grenadines”[Mesh:noexp] OR
Samoa[Mesh:noexp] OR Saudi Arabia[Mesh:noexp] OR
Senegal[Mesh:noexp] OR Serbia[Mesh:noexp] OR
Montenegro[Mesh:noexp] OR Seychelles[Mesh:noexp] OR
Sierra Leone[Mesh:noexp] OR Slovenia[Mesh:noexp] OR
Sri Lanka[Mesh:noexp] OR Somalia[Mesh:noexp] OR
South Africa[Mesh:noexp] OR Sudan[Mesh:noexp] OR
Suriname[Mesh:noexp] OR Swaziland[Mesh:noexp] OR
Syria[Mesh:noexp] OR Tajikistan[Mesh:noexp] OR
Tanzania[Mesh:noexp] OR Thailand[Mesh:noexp] OR
Togo[Mesh:noexp] OR Tonga[Mesh:noexp] OR “Trinidad
and Tobago”[Mesh:noexp] OR Tunisia[Mesh:noexp] OR
Turkey[Mesh:noexp] OR Turkmenistan[Mesh:noexp] OR
Uganda[Mesh:noexp] OR Ukraine[Mesh:noexp] OR
Uruguay[Mesh:noexp] OR USSR[Mesh:noexp] OR
Uzbekistan[Mesh:noexp] OR Vanuatu[Mesh:noexp] OR
Venezuela[Mesh:noexp] OR Vietnam[Mesh:noexp] OR
Yemen[Mesh:noexp] OR Yugoslavia[Mesh:noexp] OR
Zaire[Mesh:noexp] OR Zambia[Mesh:noexp] OR
Zimbabwe[Mesh:noexp])
141 The Campbell Collaboration | www.campbellcollaboration.org
APPENDIX 3: SEARCH DIARY
Databases Using PUBMED Search Strategy 9-3-2013: PUB MED: Total hits 1320 9-3-2013: POPLINE: Total hits 86 9-3-2013: PYSCHMED: total hits 67 3-20-2013 3ie Database: 17 articles found; 4 included 3-8-2013 JOLIS : IMF: 392 articles; none included 3-9-2013 JOLIS: World Bank: 1239 Results; 11 included Using Alternative Search Strategies: 3-1-2013 PROQuest Social Sciences: women OR woman OR Female OR Girl OR Self-help OR self help OR support OR empower OR women’s empowerment OR girl’s empowerment OR empowering OR power OR control OR decision-making OR choice OR violence OR cooperative OR collective OR program OR Group OR organization, 2046 found; 0 included 4-3-2012: IBSS International Bibliography of Social Sciences/Proquest, 431 results, 7 included INDMED: Searched: group AND economic; women AND group; woman OR women OR female OR girl AND group OR cooperative OR program OR collective AND empowerment OR empower OR empowering OR power OR control OR choice OR violence: 0 found, 0 included Search: empowerment: Total HITS: 18, Included: 6 12-31-2012 Index Medicus for the WHO http://www.globalhealthlibrary.net : Search: woman OR women OR female OR girl AND group OR cooperative OR program OR collective AND empowerment OR empower OR empowering OR power OR control OR choice OR violence: 29 results; 0 included Search: empowerment AND group AND women, 207 results; Included: 14 BLDS: Search: woman OR women OR female OR girl AND group OR cooperative OR program OR collective AND empowerment OR empower OR empowering OR power OR control OR choice OR violence: No results Search: “empowerment AND group” : 15 results; Included 7 Search: “women AND empower AND group” returned 3 results – already included relevant results Search: “women AND empowerment AND group ” returned 10 results – already included relevant results Search: “women AND group” returned 219 results, Included: none AFRICABIB http://www.africabib.org/: Search: empowerment AND group: Found: 17, Included: 1; Search: empowerment : Found: 449, Included: 2 African Women Bibliographic Database, Search: empowerment AND group: Found: 4, Included: 0; Search: empowerment : Found: 439, Included: 2 Women Travelers Bibliographic Database: Search: empowerment AND group: Found: 0, Included: 0; Search: empowerment : Found: 0, Included: 0 Islam in Contemporary Sub-Saharan Africa, Search: empowerment: Found: 5, Included: 0 Kenya Coast Bibliographic Database, Search: empowerment: Found: 0 EconLit, Search: empowerment : Found: 4, included: 0 FEMNET: Hits: 3, Included: 0 Mulitlateral Organizations Keywords Search: Women AND Group AND Empower* 10-18-13: WHO website, 24 articles searched; 3 include; 5 maybe 10-15-13: USAID, 9 articles searched; 2 maybe 10-14-13: United Nations Development Fund, 13 articles searched; 2 maybe studies; 0 include 10-12-13: United Kingdom of International Development, 25 articles searched; 6 studies included; 1 maybe 7-6-2013: Journal of International Development, 10 articles searched; 4 included 6-7-2013: African Development Bank, 24 articles searched; 3 included; 3 Maybe
142 The Campbell Collaboration | www.campbellcollaboration.org
3-29-2012 Journal: Economic development and cultural change, 2005-2013: 5 included 12-27-2012 Google Scholar; 8 included 12-28-2012 Asian Development Bank; 3 included, African Development Bank; 5 included UNICEF; 3 included, United Nations Development Programme; 4 included, 12-17-2012: United Nations Fund for Population; 4 included United Nations Development Fund For Women; 9 included, 12-31-2012, UNDP, 3 included 12-28-2012, African Development Bank, 4 included UNICEF; 3 included 12-27-2012, United Nations Development Programme; 4 included 12-17-2012, United Nations Fund for Population; 4 included United Nations Development Fund For Women, 9 included Inter-American Development Bank, 2 included International Food Policy Research Institute, 1 included 12/21/2012, United Kingdom’s Department for International Development ; 13 included United States Agency for International Development ; 14 included World Bank; 3 included International Fund for Agricultural Development; 2 included World Health Organization; 4 included Inter-American Development Bank; 2 included International Food Policy Research Institute; 1 included
Hand Search of Websites SEWA, 1 included AED Center for Gender Equality, 0 Included Asian Women’s Network on Gender and Development, 0 Included The Center for the Evaluation of Global Action, 0 Included Ford Foundation, 0 Included Global Fund for Women, 0 Included GROOTS International, 1 included The Guttmacher Institute, 0 Included The Hewlett Foundation, 0 Included International Committee for Research on Women, 3 included Latin American Women and Habitat Network, 0 Included The Packard Foundation, 0 Included UCGHI Center of Expertise on Women’s Health and Empowerment, 0 Included Women Deliver, 1 included Journal Search in Library 12-12-12: Health Policy, 0 included Global Public Health, 0 included Indian Journal of Gender Studies: Total hits: 606; Included: 20 07-11-13: Third World Quarterly: Total hits: 793, Included: 6 7-17-13: Development & Change total hits: 732: Included: 13 Health Care for Women International; 3 included Development; 15 included Journal of Development Economics, 0 included International Journal of Sustainable Development, 0 included Indian Growth and Development Review, 1 included Journal of International Development , 9 included 07-11-13: Third World Quarterly, Total hits: 354, Included: 6 Current Anthropology, 4 hits, 0 included Economic Development and Cultural Change, 3 hits 0 included Feminist Economics, 85 hits, 1 included Indian Journal of Gender Studies, 0 included International Journal of Health Planning and Management, 13 hits, 0 included International Journal of Social Research Methodology, 0 included Qualitative Research in Psychology, 0 included World Development, 0 included
Key Word Search
143 The Campbell Collaboration | www.campbellcollaboration.org
2-21-2013: Search: Mexico Indigenous People’s Development Project; 1 included Search: Professional Assistance for Development Action; 1 included Search: Self Employed Womens Association; 1 included Search: Swarnjayanthi Gram SwarozgarYojana; 3 included 1-24-2013: Search: Livelihood Empowerment Against Poverty; 3 included Search: Productive Safety Net Programme self-help group women's empowerment; 1 included Search: Comprehensive Africa Agricultural Development Programme; 1 included Search: Colombia Humanitarian women's empowerment economic self-help group; 1 included Search Google Scholar 2 pages: Grassroots Women Environmental Protection and Poverty Alleviation Project; 2 included 1-16-2013: Search: Division of Advancement for Women; 3 included Search: Gender Equity Model Egypt; 1 included Search: Redcamif; 0 included Search: Promujer; 2 included 1-15-2013: Key Contact added: Ushma Uppaday; 1 included 1-10-2013: Search: Progresa; 3 included Search: The Self-Employed Women’s Association (SEWA); 1 included Search: The Social Entrepreneurship Program; 1 included Search: Inter-American Center for Research and Documentation on Professional Training; 0 included Search: Womens World Banking; 0 included Search: OAS; 0 included Search: Grameen Bank india micro finance; 5 included 1-5-2013: Bangladesh Rural Advancement Committee (BRAC) India Micro finance; 7 included 1-10-2013: Progressa; 2 included Search: The Self-Employed Women’s Association (SEWA); 1 included Search: The Social Entrepreneurship Program; 1 included Search: Inter-American Center for Research and Documentation on Professional Training; 0 included Search: OAS; 0 included Search: Grameen Bank india micro finance; 5 included 1-5-2013: Search: Bangladesh Rural Advancement Committee (BRAC) India Micro finance; 7 included Search: the GSMA mWomen Programme, 1 included Search: Hauirou Commission, 1 included Search: The Peri-Urban Interface, 1 included
144 The Campbell Collaboration | www.campbellcollaboration.org
APPENDIX 4: REASONS FOR EXCLUSION OF MARGINAL
STUDIES
Quantitative Reason for Exclusion
Ackerly, 1995 This study does not have a valid comparison group.
Ashburn, 2007 This study does not focus on empowerment outcomes.
Banerjee, 2004 No quantitative estimate of impact
Bushamuka et al, 2005 This is not an evaluation of a self-help group program
Chandra and Sinha, 2010 This study does not have a valid comparison group and outcomes are not measured at the woman level
Deininger and Liu, 2009 No focus on empowerment
Euser et al., 2012 There is no clear comparison group
Feigenberg, 2010 No focus on empowerment and this is an RCT but the control group also consists of members of SHGs
Lokhande, 2013 There is no clear comparison group
Madheswaran, 2001 No quantitative estimate of impact
Mansuri, 2010 There is no clear comparison group
Mayoux, 2005 No quantitative estimate of impact
Murthy 2012 This is not an evaluation of a self-help group program
Odutolu et al., 2003 No quantitative estimate of impact
Oosterhoff et al., 2008 No quantitative estimate of impact
Panda, 2009 This study does not focus on empowerment outcomes.
Parajuli, 2012 This is not an evaluation of a self-help group program
Premaratne et al., 2012 There is no clear comparison group
Pronyk et al., 2008 This study does not focus on empowerment outcomes.
Pucho, 2008 This study does not have a valid comparison group.
Reddy, 2005 No quantitative estimate of impact
Sabhlok, 2011 No quantitative estimate of impact
Salway, 2005 This is not an evaluation of a self-help group program
Sinha, Parida and Baurah, 2012 This study does not have a valid comparison group.
Sinha, Pastakia, 2004 No quantitative estimate of impact
Suguna, 2006 This study does not have a valid comparison group.
Swain and Varghese, 2009 This study does not focus on empowerment outcomes.
Teshome et al., 2006 No quantitative estimate of impact
UNFPA, 2006 No quantitative estimate of impact
Urquieta et al., 2009 This is not an evaluation of a self-help group program
Uys, Benghu, and Majumdar, 2006 No quantitative estimate of impact
Van Kempen, 2009 This study does not have a valid comparison group.
145 The Campbell Collaboration | www.campbellcollaboration.org
Vijayanthi, 2002 No quantitative estimate of impact
Qualitative Reason
Ahmed, 2011 This is not an evaluation of a self-help group program
Apantaku, 2008 This is not an evaluation of a self-help group program
Barry, 2012 This study does not focus on empowerment outcomes.
Bhat, 2001 This is not an evaluation of a self-help group program
Bhengu, 2010 This is not an evaluation of a self-help group program
Biradavolu, 2009 This study does not report direct quotations from participants
Cheston, 2002 This is not an evaluation of a self-help group program
Faraizi, 2011 This study does not report direct quotations from participants
Ghadoliya, 2003 This is not an evaluation of a self-help group program
Gibb, 2008 This is not an evaluation of a self-help group program
Guerin, 2006 This study does not report direct quotations from participants
Hoodfar, 2010 This is not an evaluation of a self-help group program
Islam, 2011 This is not an evaluation of a self-help group program
Jerinabi, 2008 This study does not report direct quotations from participants
Kim, 2007 This is not an evaluation of a self-help group program
Kuttab, 2010 This is not an evaluation of a self-help group program
Lokhande 2008 This is not an evaluation of a self-help group program
Lombe, 2012 This study does not report direct quotations from women participants
Mayoux, 2000 This is not an evaluation of a self-help group program
Mayoux, 2001 This study does not report direct quotations from participants
Meena, 2011 This is not an evaluation of a self-help group program
Moyle 2006 This study does not report direct quotations from participants
Ndinda, 2009 This is not an evaluation of a self-help group program
Nguyen, 2009 This is not an evaluation of an self-help group program
Nkosi, 2003 This study does not report direct quotations from participants
Noreen, 2011 This is not an evaluation of a self-help group program
Norwood, 2004 This study does not report direct quotations from participants
Panwar, 2010 This is not an evaluation of a self-help group program
Patel, 2010 This study does not report direct quotations from participants
Pronyk, 2008a This is not an evaluation of a self-help group program
Pronyk, 2008b This is not an evaluation of a self-help group program
Rahman, 2011 This is not an evaluation of a self-help group program
Reza-Paul, 2012 This is not an evaluation of a self-help group program
Roger, 2011 This is not an evaluation of a self-help group program
146 The Campbell Collaboration | www.campbellcollaboration.org
Sabhlok, 2011 This study does not focus on empowerment outcomes.
Salway 2005 This is not an evaluation of a self-help group program
Sarojani, 2009 This study does not report direct quotations from participants
Sharma, 2014 This is not an evaluation of a self-help group program
Shylendra, 1999 This study does not report direct quotations from participants
Somé, 2013 This is not an evaluation of a self-help group program
Sotshongaye, 2000 This is not an evaluation of a self-help group program
Ssewamala, 2009 This is not an evaluation of a self-help group program
Torri, 2011 This is not an evaluation of a self-help group program
Tupe, 2013 This study does not report direct quotations from participants
Vijayanthi, 2002 This study does not report direct quotations from participants
147 The Campbell Collaboration | www.campbellcollaboration.org
APPENDIX 5: QUALITATIVE STUDY QUALITY ASSESSMENT
Screening Question: Is there a clear
statement of study aims of the research?
Yes / Can’t tell / No
Screening Question: Is a qualitative
methodology appropriate?
Yes / Can’t tell / No
Is it worth continuing?
Was the research design appropriate to address
the aims of the research?
Yes / Can’t tell / No
Was the recruitment strategy appropriate to
address the aims of the research?
Yes / Can’t tell / No
Was the data collected in a way that addressed
the research question?
Yes / Can’t tell / No
Has the relationship between researcher and
participants been adequately considered?
Yes / Can’t tell / No
Have ethical issues been taken into
consideration?
Yes / Can’t tell / No
Was the data analysis sufficiently rigorous? Yes / Can’t tell / No
Is there a clear statement of findings? Yes / Can’t tell / No
How valuable is the research?
SOURCE: Critical Appraisal Skills Programme (2013). “Qualitative Checklist.” Oxford,
United Kingdom. Accessed from:
http://media.wix.com/ugd/dded87_342758a916222fedf6e2355e17782256.pdf.
148 The Campbell Collaboration | www.campbellcollaboration.org
Study Name Dahal Kabeer Kilby Knowles Kumari Sahu Maclean Mathrani Mercer Pattenden Ramachandar
Was there a clear statement of the aims of the study?
yes yes Yes yes Yes yes Yes Yes Yes Yes yes
Is the qualitative methodology appropriate?
yes yes Yes yes Yes yes Yes Yes Yes Yes yes
Was the research deign appropriate to address the aims of the research?
yes yes Yes yes Yes yes Yes Yes Yes Yes yes
Was the recruitment strategy appropriate to the aims of the research?
yes yes Yes yes Yes no Yes Yes Yes Yes yes
Was the data collected in a way that addressed the research issue?
yes yes Yes yes Yes yes Yes Yes Yes Yes yes
Has the relationship between researcher and participants been adequately considered?
yes Can't Tell Can't Tell yes yes Can't Tell yes Can't tell No Can't tell Can't Tell
Have ethical issues been taken into consideration?
yes Can't Tell No yes No Can't Tell No Can't tell No No yes
Was the data analysis sufficiently rigorous?
yes yes Yes yes Yes yes Yes Yes Yes Yes yes
149 The Campbell Collaboration | www.campbellcollaboration.org
Is there a clear statement of findings?
yes yes Yes yes Yes yes Yes Yes Yes Yes yes
Is the research valuable? Valuable Valuable Valuable Valuable Valuable Valuable Valuable Valuable Valuable Valuable Valuable
150 The Campbell Collaboration | www.campbellcollaboration.org
APPENDIX 6: QUANTITATIVE RISK OF BIAS ASSESSMENT
TOOL
Code description Code Comment
Study ID Last name of author,
year
Justification of use Study design and
methodology
Ask these questions for all quantitative studies
Does the study show baseline characteristics of beneficiaries
and non-beneficiaries?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
If baseline characteristics are not available, does the study show
characteristics of beneficiaries and non-beneficiaries that are
not likely to be affected by the intervention?
Are the mean values or the distributions of the covariates at
baseline statistically different for beneficiaries and non-
beneficiaries (p<0.05)
If there are statistically significant differences in plausibly
exogenous characteristics between beneficiaries and non-
beneficiaries are these differences controlled for using covariate
analysis in the impact evaluation?
If baseline characteristics are not available, does the study
qualitatively assess why beneficiaries are likely/unlikely to be a
random draw of the population at baseline?
Confounding and selection bias (ask questions for all
quantitative studies)
Does the study use a comparison/control group of
students/households without access to the program?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Does the study use a comparison/control group of
students/households with access to the program but without
participation in the program?
Does the study include data at baseline and endline (before and
after the intervention)?
Are the data on covariates collected at the baseline?
Is difference in differences estimation (i.e. using statistical
inference) used?
If the study is quasi-experimental and uses difference-in-
difference estimation do the authors assess the parallel trends
assumption?
If the study does not use difference in difference, does the study
control for baseline values of the outcome of interest
If the study does not use difference in difference and does not
control for baseline values of the outcome variable, does the
study control for other covariates at baseline
If the study does not use difference in differences estimation, is
there any assessment of likely risk of bias from time invariant
characteristics driving both participation and outcome?
If the study does not use difference in difference estimation but
does assess likely risk of bias from time invariant
151 The Campbell Collaboration | www.campbellcollaboration.org
characteristics, are these time invariant characteristics likely to
bias the impact estimates
Does the study report the table with the results of the outcome
equation (including covariates)? Where full results of the
outcome equation are not reported, is it clear which covariates
have been used?
Are all relevant observable covariates (confounding variables)
included in the outcome equation which might explain
outcomes, if estimation does not use a statistical technique to
control for selection bias (RCT, PSM or covariate matching, IV
or switching regression)? This might, for example, include
control for ability, and/or social capital.
Attrition (ask questions for all quantitative studies)
For studies including baseline data, does the study report
attrition (drop-out) rates?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Is the attrition rate below 10% ?
Does the study assess whether drop-outs are random draws
from the sample (e.g. by examining correlation with
determinants of outcomes, in both treatment comparison
group)?
Spillovers and contamination (ask questions for all
quantitative studies)
Spillovers: are comparisons sufficiently isolated from the
intervention (e.g., participants and non-participants are
sufficiently geographically or socially separated) or are
spillovers estimated by comparing non-beneficiaries with
access to the intervention to non-beneficiaries without access to
the intervention and/or through social network analysis?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Spillovers; if spillovers are not estimated, is the study likely to
bias the impact of the program?
Contamination: does the study assess whether the control group
receives the intervention?
Contamination: if the control group receives the intervention
but for a shorter amount of time does the study assess the
likelihood that the control group has received equal benefits as
the treatment group
Contamination: if the control group receives the intervention
have they received the intervention sufficiently long to argue
that they have benefited from the intervention
Contamination: does the study describe and control for other
interventions which might explain changes in outcomes?
Other threats to validity (ask questions for all
quantitative studies)
Does the evidence suggest analysis reporting biases are a
serious concern? Analysis reporting biases include failure to
report important treatment effects (possibly relating to
intermediate outcomes), or justification for (uncommon)
estimation methods, especially multivariate analysis for
outcomes equations.
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Are there concerns about baseline data collected retrospectively
Are there concerns about courtesy bias, social acceptability bias,
political correctness bias, self-serving bias, self-importance bias
and biases in reporting of sensitive information from outcomes
collected through self-reporting?
152 The Campbell Collaboration | www.campbellcollaboration.org
Construct Validity (ask questions for all quantitative
studies)
Was the survey suitable for the local context? 1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer Does the study describe the implementation of the program in
sufficient detail?
Does the study take into consideration potential
implementation failures
Does the study use a proper theory of change, logframe
and/or other proper conceptual or theoretical framework?
Does the study analyze the outcome measures put forward
in the theory of change or logframe?
Was the implementation of the intervention influenced by the
research?
Did the researchers have perfect control over the intervention?
Was the implementing agency representative for the agencies
that usually implement self-help group programs?
External Validity (ask questions for all quantitative
studies)
Is the study sample representative of the population of
interest?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Was the effectiveness of the intervention harmed by
implementation failures that would not have happened in the
absence of the research?
Does the study assess the replicability of the intervention?
Is the intervention replicable?
Does the study assess the scalability of the intervention?
Is the intervention scalable?
Do the authors clearly distinguish between the intention-to-
treat effect and the treatment effect on the treated?
Do the authors highlight the intention-to-treat effect?
Hawthorne and John Hendry Effects (ask questions
for all quantitative studies)
Do the authors argue convincingly that it is not likely that
being monitored influences the behavior of the beneficiaries
and non-beneficiaries in different ways?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Confidence Intervals (ask questions for all
quantitative studies)
Does the study account for lack of independence between
observations within assignment clusters if the outcome
variables are clustered?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Is the sample size likely to be sufficient to find significant effects
of the intervention?
Do the authors control for heteroskedasticity and/or use robust
standard errors?
Ask questions below only for studies that apply
randomization
Does the study apply randomized assignment? 1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer Does the study use a unit of allocation with a sufficiently large
sample size to ensure equivalence between the treatment and
the control group?
Ask questions below only for studies that apply
regression discontinuity designs
153 The Campbell Collaboration | www.campbellcollaboration.org
Is the allocation of the program based on a pre-determined
continuity on a continuous variable and blinded to the
beneficiaries or if not blinded, individuals cannot reasonably
affect the assignment variable in response to knowledge of the
participation rule?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Is the sample size immediately at both sides of the cut-off point
sufficiently large to equate groups on average?
Is the mean of the covariates of individuals immediately at both
sides of the cut-off point statistically significantly different for
beneficiaries and non-beneficiaries?
If there are statistically significant differences between
beneficiaries and non-beneficiaries are these differences
controlled for using covariate analysis?
Ask questions below only for studies that apply
matching
Quality of matching (PSM, covariate matching)
Are beneficiaries and non-beneficiaries matched on all relevant
characteristics?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Does the study report the results of the matching function (e.g.
for PSM the logit function)?
Does the study report the matching method?
Does the study exclude observations outside the common
support?
Does the study use variables at follow-up that can be affected by
the intervention in the matching equation?
Are matches found for the majority of participants (>90% )?
If >=10% of participants failed to be matched, is sensitivity
analysis used to re-estimate results using different matching
methods?
For nearest-neighbor PSM, does the study report the mean or
distribution of the propensity scores in the treatment and
control groups after matching?
For nearest-neighbor PSM, are propensity scores similar, based
on tests for statistical differences at the means or other
quantiles of the distribution)?
Does the study report the mean or distribution for the
covariates of the treatment and control groups after matching?
Are these characteristics similar, based on tests for statistically
significant differences (p>0.5)?
Do the authors use bootstrapped standard errors?
Sensitivity analysis (only for studies that apply PSM)
For PSM, where propensity score distributions and/or
covariates of the treatment and control groups are not reported,
or they are reported but there are differences in means or
distributions of the covariates or propensity scores (usually only
applicable to methods which do not exclude treatment
observations such as nearest neighbor), is robustness assessed
using an additional matching technique?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Is sensitivity to hidden bias assessed statistically, e.g., using the
Rosenbaum bounds test?
Ask questions below only for studies that apply
instrumental variable estimation
Quality of IV, two-steps endogenous switching
regression approach
154 The Campbell Collaboration | www.campbellcollaboration.org
Does the study describe clearly the instrumental
variable(s)/identifier used?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
Are the results of the participation equation reported?
Are the instruments jointly significant at the level of F ≥ 10? If
an F test is not reported, does the author report and assess
whether the R-squared of the instrumenting equation is large
enough for appropriate identification (R-sq > 0.5? )
Are the instruments individually significant (p≤0.05 )?
For IV, If more than one instrument is used in the procedure,
does the study include and report an overidentifying test
(p≤0.05 is required to reject the null hypothesis)?
Does the study qualitatively assess the exogeneity of the
instrument/identifier (both externality as well as why the
variable should not enter by itself in the outcome equation)?
Ask questions below only for studies with censored
outcome variables
Do the authors use appropriate methods (e.g. Heckman
selection models, tobit models, duration models) to account for
the censoring of the data?
1 = Yes
2 = No
9 = Unclear
99 = Not applicable
Comment: Open
answer
For Heckman models; is there is a variable that is statistically
significant in the first stage of the selection equation and
excluded from the second stage
Overall Assessment
Assessment Selection Bias Low risk of bias
Medium risk of bias
High risk of bias
Unclear risk of bias
Comment: Open
answer Assessment Spillovers and Contamination Bias
Assessment Outcome and Analysis Reporting Bias
Assessment Other biases
155 The Campbell Collaboration | www.campbellcollaboration.org
APPENDIX 7: OVERVIEW OF RISK OF BIAS ASSESSMENT OF
INCLUDED QUANTITATIVE STUDIES
Selection Bias and Confounding
Performance Bias: Assessment Spillovers and Contamination
Outcome and Analysis Reporting Biases
Other Biases
Ahmed, 2005 High risk of bias High risk of bias Medium risk of bias
Medium risk of bias
Swain and Wallentin, 2009
High risk of bias High risk of bias High risk of bias High risk of bias
Banerjee et al., 2015, 2010
Low risk of bias Medium risk of bias
Low risk of bias Low risk of bias
Coleman, 1999 High risk of bias High risk of bias Low risk of bias Low risk of bias
De Hoop et al., 2014
Medium risk of bias
High risk of bias Low risk of bias Medium risk of bias
Deininger and Liu, 2013, 2009
Medium risk of bias
Low risk of bias Medium risk of bias
High risk of bias
Desai and Joshi, 2012
Low risk of bias Low risk of bias Low risk of bias Low risk of bias
Desai and Tarozzi, 2011
Low risk of bias Low risk of bias Low risk of bias Medium risk of bias
Garikipati 2012, 2008
High risk of bias High risk of bias High risk of bias Low risk of bias
Holvoet, 2005 High risk of bias Medium risk of bias
Low risk of bias Medium risk of bias
Husain et al., 2010 High risk of bias High risk of bias High risk of bias High risk of bias
Kim et al., 2009 and Pronyk et al., 2006
Medium risk of bias
Low risk of bias Medium risk of bias
Medium risk of bias
Kundu, et al., 2011 High risk of bias High risk of bias High risk of bias High risk of bias
Mahmud, 1994 High risk of bias High risk of bias High risk of bias Medium risk of bias
Nessa et al., 2012 High risk of bias High risk of bias High Risk of Bias Low risk of bias
Osmani, 2007 High risk of bias High risk of bias High risk of bias Low risk of bias
Pitt et al., 2006 Medium risk of bias
High risk of bias Medium risk of bias
Low risk of bias
Sherman et al., 2010
Medium risk of bias
High risk of bias High risk of bias High risk of bias
Steele et al., 1998 High risk of bias High risk of bias High risk of bias Low risk of bias
156 The Campbell Collaboration | www.campbellcollaboration.org
Swendeman et al., 2009
High risk of bias Low risk of bias Medium risk of bias
Medium risk of bias
APPENDIX 8: DETAILED RISK OF BIAS ASSESSMENT OF
INCLUDED QUANTITATIVE STUDIES
Selection Bias and Confounding
Performance Bias: Assessment, Spillovers, and Contamination
Outcome and Analysis Reporting Biases
Other Biases
Ahmed, 2005
High risk of bias High risk of bias Medium risk of bias Medium risk of bias
The study does not adequately control for selection bias in the analysis.
The study does not take into consideration that the comparison group may also have been contaminated by the intervention.
The study assesses the impact of several components of the intervention without taking into consideration selection bias. This is an uncommon estimation method, which suggests that the analysis is vulnerable to analysis reporting biases. The study also uses co-variates in the model that may be endogenous.
The answers to the questions about domestic violence are vulnerable to social desirability bias.
Bali Swain and Wallentin, 2009
High risk of bias High risk of bias High risk of bias High risk of bias
The study uses analysis to separately determine the trend of the outcome measures among the beneficiaries and the non-beneficiaries. This does not allow for estimating the impact of the intervention. Hence, the study does not use a valid identification strategy
The study selects the comparison group from the same location as the beneficiaries so there is a potential for spillovers biasing the findings.
The study uses an unusual type of analysis (separately determining the trend for the beneficiaries and the non-beneficiaries). This could bias the research findings.
The use of recall data could bias the impact estimates. And it is not well explained why the use of these data can be considered valid for this study.
Banerjee et al., 2015, 2010
Low risk of bias Medium risk of bias Low risk of bias Low risk of bias
The study uses a matched-pair cluster-randomized controlled trial. Baseline and follow-up data were collected, but panel data were not available (i.e., the respondents in the follow-up are not necessarily the same as the respondents at baseline due to resampling). The study assesses equivalence of
The control group was contaminated by other types of microfinance. The study notes that other microfinance interventions were rolled out during the study period in both treatment and control areas and notes that both treatment and
There do not appear to be serious outcome or analysis reporting biases.
There do not appear to be serious other biases. The outcome measures do not seem to be vulnerable to social desirability bias.
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treatment and control areas at baseline and endline and does not find significant differences. The study controls for clustering in the calculation of the standard errors. Other microfinance interventions were rolled out during the study period in both treatment and control areas. Both treatment and comparison areas were borrowing microcredit (though borrowing rates were lower in the comparison area). However, the study does not control for the other microfinance interventions in the analysis. Instead, the authors calculate an intention-to-treat effect.
comparison areas were borrowing microcredit (thought borrowing rates were lower in comparison area). However, the study does not control for the other intervention effects in the analysis.
Coleman, 2002
High risk of bias High risk of bias Low risk of bias Low risk of bias
The study uses a multivariate regression model that includes a dummy for participation in a self-help group, and a variable capturing the number of months during which self-help group members received credit as explanatory variables. Although the authors claim that this methodology allows for controlling for selection bias, this methodology cannot be considered a credible identification strategy. First, it is not clear how the beneficiaries of the intervention were selected. Second, there may have been self-selection among those beneficiaries who started benefiting from the intervention at an early stage. Third, the study does not control for selection bias based on unobservables. These problems cannot be resolved by including village fixed effects.
The comparison group includes non-beneficiaries who could have been affected by the intervention due to their close proximity to the beneficiaries of the intervention. Hence, the findings of the evaluation could be biased due to spillovers.
There do not appear to be serious outcome or analysis reporting biases.
There do not appear to be serious other biases. The outcome measures do not seem to be vulnerable to social desirability bias.
De Hoop et al., 2014
Medium risk of bias High risk of bias Low risk of bias Medium risk of bias
The study uses a propensity score matching design without baseline
There is a potential bias from spillover effects as the non-
There do not appear to be serious outcome or analysis reporting
The answers to the questions about domestic
158 The Campbell Collaboration | www.campbellcollaboration.org
data. For the nearest neighbor matching, the study does not report the mean or distribution of the propensity scores in the treatment and control groups after matching, or the mean or distribution for the covariates of the treatment and control groups after matching, but the study does control for robustness of the results using kernel matching.
members (the comparison group) are drawn from the same villages as the SHG members (treatment group).
biases now that the authors have responded with a set of analyses with additional outcome measures.
violence are vulnerable to social desirability bias.
Deininger and Liu, 2013, 2009
Medium risk of bias Low risk of bias Medium risk of bias High risk of bias
The study uses propensity score matching and difference-in-difference estimation. The study uses recall data over a four-year recall period for the DID estimation component. This may result in bias.
The authors estimate a combined intention-to-treat effect for women who decide to self-select into SHGs and women who decide not to self-selection into SHGs. This minimizes the risk of spillovers.
The two versions of this paper report slightly different results, which may indicate outcome reporting bias. Standard deviations are not reported in either version of the paper, and authors did not respond to requests for information. As a result, the standard deviations had to be imputed increasing the potential risk of bias of the effect size.
The use of recall data could result in social desirability bias in the measurement of empowerment.
Desai and Joshi, 2012
Low risk of bias Low risk of bias Low risk of bias Low risk of bias
It appears that the randomization resulted in balance across observable and unobservable characteristics.
The authors estimate a combined intention-to-treat effect for women who decide to self-select into SHGs and women who decide not to self-select into SHGs. This minimizes the risk of spillovers.
There do not appear to be serious outcome or analysis reporting biases.
There do not appear to be other serious biases.
Desai and Tarozzi, 2011
Low risk of bias Low risk of bias Low risk of bias Medium risk of bias
The randomized controlled trial design suggests there is balance across observable and unobservable characteristics, and the balance test suggests that the randomization has worked, although not perfectly due to noncompliance. Nonetheless, the researchers choose a valid
There do not appear to be serious concerns about spillovers.
There do not appear to be serious concerns about outcome or analysis reporting biases.
Condom use is a sensitive variable. This could increase measurement error. This is not discussed in the paper.
159 The Campbell Collaboration | www.campbellcollaboration.org
instrumental variable approach to account for the noncompliance with the randomization. The study collects baseline and endline data at the individual level, but not for the same individuals. The authors therefore estimate mean effects at the village level, thus considerably reducing the power of the study—the sample size is only 54 PAs in Amhara, and 78 PAs in Oromia, which may be insufficient to detect small/medium-sized effects. However, the authors control for several plausibly exogenous control variables, which should normally increase the statistical power of the study.
Garikipati, 2012, 2008
High risk of bias High risk of bias High risk of bias Low risk of bias
The study uses a cross-sectional design and instrumental variables estimation to address selection bias. The validity of the instrumental variable is not discussed or tested, which increases the risk of bias. The study does not measure covariates or outcomes at baseline. It does not take into account clustering in the analysis and does not report the use of cluster-robust standard errors. The authors include the “own use of loan” as an explanatory variable. This intermediate outcome variable should not have been included in the outcome equation.
Spillovers can bias the findings of this study because the non-beneficiaries come from the same village and may also have been affected by the intervention.
The study from 2008 uses unusual methods to construct the outcome variables. This may result in outcome reporting biases. Furthermore, the use of intermediate outcome variables as explanatory variables could result in analysis reporting biases.
There do not appear to be serious other biases.
Holvoet, 2005
High risk of bias Medium risk of bias Low risk of bias Medium risk of bias
This is an ex post multivariate multinomial logistic regression study without a valid identification strategy. The study does not collect baseline data and elicits baseline characteristics using recall over long periods. The study attempts to "match" the programs that deliver
There was potential for spillover effects, but the study reports that the authors attempted to minimize these by not sampling non-beneficiaries with close connections to the beneficiaries.
It does not appear that there are serious outcome or analysis reporting biases.
The study relies on retrospective baseline data collection to a considerable extent.
160 The Campbell Collaboration | www.campbellcollaboration.org
the treatments under study, but does not match participants nor otherwise control for selection bias in the analysis. Furthermore, the study does not use a dummy variable for membership as the treatment variable but the time women are members of self-help groups. This type of analysis does not take into consideration the possibility of nonlinearities.
Husain et al., 2010
High risk of bias High risk of bias High risk of bias High risk of bias
The study uses multivariate regression analysis without a valid identification strategy. The study compares new to old SHG members and does not collect outcome or covariate data at baseline. This makes it impossible to reliably evaluate the effectiveness of the program and extract reliable effect sizes. The study does also not control for selection bias and does not take clustering into consideration in the calculation of the standard errors.
The new members and old members appear to be selected from the same locations, suggesting that bias resulting from spillovers is an important concern.
The study does not report the numeric value of the correlation coefficients, only whether these are positive or negative and statistically significant or not.
The municipalities from which the sample was selected were chosen by the implementing agency based on their successful performance, suggesting that the results may not be representative of the target population.
Kim et al., 2009, and Pronyk et al., 2006
Medium risk of bias Low risk of bias Medium risk of bias Medium risk of bias
It is not clear whether the randomization was successful. The randomization was based on a relatively small sample of four treatment and four control villages. This increases the likelihood of observable and unobservable differences between the treatment and the control group. Furthermore, it is unclear how comparable the villages with only microfinance are.
The risk of spillovers is minimized because the control villages do not have access to the intervention.
The two studies report slightly different results and sample sizes, which may indicate outcome reporting bias.
The authors note several potential limitations, including the possibility of Hawthorne or other reporting biases.
Kundu et al., 2011
High risk of bias High risk of bias High risk of bias High risk of bias
In the most recent version of this paper, the study uses a multinomial logit regression analysis without a valid identification
The study uses both nonparticipants from treatment villages and nonparticipants from control villages
In the most recent version of this paper, the study does not report the results of the multinomial logit model.
The baseline data were collected retrospectively, asking participants to
161 The Campbell Collaboration | www.campbellcollaboration.org
strategy. The authors do not use baseline data and do not report the results of the analysis. In the earlier version of the paper, the study uses panel data and difference-in-differences analysis. However, the intervention already started before the baseline survey. This invalidates the parallel trends assumption. The authors do also not take clustering into consideration in the estimation of the standard errors and do not assess the potential biases in outcome measurement.
in their analysis without separately analyzing these, which could result in a bias due to spillovers. Furthermore, several members from the comparison group were members of self-help groups during the baseline survey, suggesting that they may also have been affected by the intervention.
In the earlier version of the paper, the study is vulnerable to analysis reporting biases because of the start of the intervention before the collection of baseline data.
recall information from two years ago.
Mahmud, 1994
High risk of bias High risk of bias High risk of bias Medium risk of bias
This is a cross-sectional study using multi-variate analysis without baseline data collection. The study does not use a valid identification strategy. The authors also do not take into consideration clustering in the calculation of the standard errors.
The control group is drawn from the same locations as the beneficiaries. Hence, the estimate of the impact of the intervention may be biased due to spillovers.
The study only assesses the impact of the program on two primary outcomes defined by the study. The authors decided not to analyze the effect of the program on the use of temporary contraception because “the bivariate frequency distributions have revealed that both the level and pattern of use of temporary methods was largely undifferentiated between the two [treatment and control] groups.” However, there were significant differences in the characteristics of the treatment and control group, so one cannot apriori assume that an absence of a difference in the unadjusted outcome necessarily translates into an absence of effect following adjustment for confounding factors. And there may be a potential for outcome reporting bias.
There do not appear to be serious concerns about other biases. It is unclear whether the outcome variable measures empowerment.
High risk of bias High risk of bias High Risk of bias Low risk of bias
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Nessa et al., 2012
The study uses a cross-sectional study design without data collection at baseline and does not have a valid identification strategy. The study only controls for a small number of potential confounding variables but also includes annual income, a variable likely affected by the program, as a control variable in the regression analysis.
The non-beneficiaries come from the same locality as the beneficiaries, which could invalidate the results due to spillovers.
The outcome variables are not well explained. It controls for a small number of potential confounding variables but also includes annual income, a variable likely affected by the program, as a control variable in the regression analysis.
There do not appear to be serious concerns about other biases.
Osmani, 2007
High risk of bias High risk of bias High risk of bias Low risk of bias
The study uses an instrumental variable approach to address the problem of selection bias. However, the validity of the instruments depends on the inclusion of household income as an independent variable, which is an intermediate outcome. The study also instruments for household income, but includes the participation variable in the equation that estimates household income. Thus, the study uses one endogenous variable to predict the other endogenous variable and vice versa, which suggests the instruments are not valid. Household income, being an intermediate outcome, should not be included in the model. The sample size is also too small to determine precise effects (42 treatment and 42 comparison households), and the study does not adjust for clustering.
The beneficiaries and the comparison group were drawn from the same villages. Hence, the estimates may be biased due to spillovers.
It appears that the authors do not apply the use of instrumental variables in a correct manner. This suggests that the findings are vulnerable to analysis reporting biases.
There do not appear to be serious other biases.
Pitt et al., 2006
Medium risk of bias High risk of bias Medium risk of bias Low risk of bias
The study uses a fixed-effects instrumental variable regression approach (and compares the results to ordinary least squares with village-level variables and fixed-effects estimation). The authors identify a set of instrumental variables and control for village-level fixed unobserved characteristics.
The authors do not discuss the potential bias from spillover effects, even though the comparison women come from the same communities.
The study does not report the participating equation; it is unclear whether the instruments were jointly or independently significant; and the authors do not report a test for overidentification.
There do not appear to be serious outcome and analysis reporting biases
163 The Campbell Collaboration | www.campbellcollaboration.org
It is unclear whether the instruments are valid.
Sherman et al., 2010
Medium risk of bias High risk of bias High risk of bias High risk of bias
The study is a randomized controlled trial. The authors adjust for age and household income at baseline in the multivariate analysis, but only for the analysis concerned with the self-reported number of sex exchange partners, which is the primary outcome of interest. The sample size (50 treatment and 50 control group members) is arguably insufficient to ensure equivalence between samples through randomization and underpowered to detect small to medium effects.
Correspondence with the authors suggests that the control group comes from the same community, which increases the vulnerability of the study to bias from spillovers. Furthermore, the participants in the treatment group received a cash transfer so it is not very clear whether the effect is really due to self-help groups.
It does not appear that there are serious outcome or analysis reporting biases. The study controls for different control variables for different outcomes, suggesting potential analysis reporting bias.
The study uses recall data for sensitive outcome measures, which could invalidate the results of the impact evaluation.
Steele et al., 1998
High risk of bias High risk of bias High risk of bias Low risk of bias
The study uses multivariate regression and controls for baseline characteristics as well as baseline values of the outcomes of interest. However, the study also controls for several potentially endogenous variables, which could result in a bias in the impact estimates. The study does not use a valid identification strategy.
The study compares beneficiaries to eligible non-beneficiaries in the same communities, so the results could be biased due to spillovers
There are serious inconsistencies in the reporting (the results reported in the text do not match those reported in the tables) and the authors only report results for some of the analyzed comparisons. The authors also mention analyses that are not reported in the study.
There do not appear to be serious other biases. One variable was collected using recall (worked for cash or kind during last year), but this is unlikely to be a serious concern.
Swendeman et al., 2009
High risk of bias Low risk of bias Medium risk of bias Medium risk of bias
The study uses multivariate regression analysis, but does not use a valid identification strategy. The study compares randomly selected participants in a town that received the intervention to randomly selected participants in a town that received the control intervention, but does not discuss whether the intervention and treatment town were comparable, does not establish equivalence of treatment and comparison group participants, and does not appropriately control for selection bias.
The comparison group seems sufficiently far away to mitigate concerns over bias from spillovers
There do not appear to be serious outcome and analysis reporting biases. Some outcome variables were not discussed because the authors do not find significant effects.
It looks like the research team influenced the fidelity of the intervention.
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APPENDIX 9: QUALITY ASSESSMENT FOR INCLUDED QUALITATIVE STUDIES
Study Name
Dahal 2014
Kabeer 2011
Kilby 2011
Knowles 2014
Kumari 2011
Maclean 2012
Mathrani 2006
Mercer 2002
Pattenden 2011
Ramachandar 2009
Sahu 2012
Was there a clear statement of the aims of the study? yes yes yes yes yes yes yes yes yes yes yes
Is the qualitative methodology appropriate? yes yes yes yes yes yes yes yes yes yes yes
Was the research deign appropriate to address the aims of the research? yes yes yes yes yes yes yes yes yes yes yes
Was the recruitment strategy appropriate to the aims of the research? yes yes yes yes yes yes yes yes yes yes yes
Was the data collected in a way that addressed the research issue? can't tell can't tell yes yes yes yes yes yes yes yes yes
Has the relationship between researcher and participants been no can't tell yes yes can't tell can't tell can't tell can't tell can't tell can't tell can't tell
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adequately considered?
Have ethical issues been taken into consideration? yes can't tell can't tell yes can't tell can't tell can't tell can't tell can't tell yes can't tell
Was the data analysis sufficiently rigorous? yes yes yes yes can't tell can't tell yes can't tell yes yes can't tell
Is there a clear statement of findings? yes yes yes yes yes yes yes yes yes yes yes
Based on the above, is the research valuable? valuable valuable valuable valuable valuable valuable valuable valuable valuable valuable valuable
166 The Campbell Collaboration | www.campbellcollaboration.org
APPENDIX 10: PROCEDURES FOR CALCULATING EFFECT
SIZES
This appendix describes the procedure for calculating the effect sizes of the included
quantitative studies.
First, we calculated standardized mean differences (Cohen’s d) by dividing the mean
difference with the pooled standard deviation by applying the formula in Equation
10.1:
(10.1) SMD = 𝑌𝑡−𝑌𝑐
𝑆𝑝
Here, SMD refers to the standardized mean differences, Yt refers to the outcome for
the treatment group, Yc refers to the outcome for the control or comparison group,
and Sp refers to the pooled standard deviation.
The pooled standard deviation Sp can be calculated or approximated (in regression
studies) using the following two formulas in Equations 10.2 and 10.3:
(10.2) Sp = √((𝑆𝐷𝑦2)∗(𝑛𝑡+𝑛𝑐−2))−(
𝛽2∗(𝑛𝑡∗𝑛𝑐)
𝑛𝑡+𝑛𝑐)
𝑛𝑡+𝑛𝑐
(10.3) Sp = √(𝑛𝑡−1)∗𝑠𝑡2 +(𝑛𝑐−1)∗𝑠𝑐2
𝑛𝑡+𝑛𝑐−2
Equation 10.2 was used for regression studies with a continuous dependent variable
for which we had information about the point estimate for the treatment variable and
the associated standard deviation. SDy refers to the standard deviation for the point
estimate from the regression, nt refers to the sample size for the treatment group, nc
refers to the sample size for the control group, and β refers to the point estimate.
Equation 10.3 was applied when there was information about the standard deviation
for the treatment group and the standard deviation for the control group. In this
formula, st refers to the standard deviation for the treatment group and sc to the
standard deviation for the control group. We assumed the same standard deviation
for the treatment and the control or comparison group when the paper only reported
the standard deviation for the full sample, treatment group, or control or comparison
group.
Then we corrected the standardized mean difference for potential bias from a small
sample size using the formula to transform Cohen’s d to Hedges’ g in Equation 10.4:
(10.4) SMDcorrected = SMDuncorrected * (1 – 3
4∗(𝑛𝑡+𝑛𝑐−2)−1)
Finally, we calculated the standard error of the standardized mean difference using
Equation 10.5:
(10.5) SE=√𝑛𝑡+𝑛𝑐
𝑛𝑐∗𝑛𝑡+
𝑆𝑀𝐷2
2∗(𝑛𝑐+𝑛𝑡)
167 The Campbell Collaboration | www.campbellcollaboration.org
For dichotomous variables, we used odds ratios and log odds ratios rather than risk
ratios because methods are available to convert the natural logarithm of odds ratios
to the standardized mean difference and vice versa, as illustrated in the formula in
Equation 10.6 (Borenstein et al., 2009):
(10.6) g = LogOddsRatio * √3
𝜋
This transformation required several statistical assumptions but it allowed for one
meta-analysis with both dichotomous and continuous variables for the same
construct. Conducting one meta-analysis for dichotomous and continuous variables
was preferable because it substantially increased the number of studies we could
include in one meta-analysis.
It was also appropriate because the included studies that analyzed continuous
variables shared goals in common with the included studies that analyzed
dichotomous variables. Borenstein et al. (2009) suggests that the transformation of
log odds ratios to standardized mean differences improves the meta-analysis as long
as the outcome variables measure the same construct. It is less important whether the
outcome variables use different measurement scales. Nonetheless, the transformation
from log odds ratios to standardized mean differences requires several statistical
assumptions (Borenstein et al., 2009).
Following the correction of the effect size, we estimated the corrected standard error
by applying the formula in Equation 10.7 for standardized mean differences that were
estimated from odds ratios:
(10.7) SEcorrected = √𝑛𝑡+𝑛𝑐
𝑛𝑡∗𝑛𝑐 +
(𝑆𝑀𝐷𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑)2
2∗(𝑛𝑡+𝑛𝑐)
To derive odds ratio from studies that applied linear probability models, we assumed
linearity in the estimation of standardized effect sizes from the linear probability
model. In practice, this meant that if we observed a mean baseline value for the
comparison group of 0.067 and an effect size of 3.1 percentage points, then we
assumed that the follow-up value for the treatment group would be
0.067+0.031=0.098 and we assumed that the follow-up value for the comparison
group would be 0.067. Using this information, we were able to estimate odds ratios
using a 2 by 2 contingency table (Lipsey & Wilson, 2001), as described in Figure 10.1:
Figure 10.1: Estimation of odds-ratios
Frequencies
Success Failure
Beneficiaries A B
Comparison Group B D
From the figure, we calculated the odds-ratio using Equation 10.8 where 𝐸𝑆 refers to
the effect size:
(10.8) 𝐸𝑆 =𝑎𝑑
𝑏𝑐
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We then calculated the standard error of the natural logarithm of the odds ratio by
calculating the number of cases where the treatment group could be considered
empowered and the number of cases where the control or comparison group could be
considered empowered. We did this by using the information about the percentage of
empowered women in the treatment and control or comparison group, and
information about the sample size in the treatment and control or comparison group.
This allowed us to estimate the standard error of the natural logarithm of the odds
ratio using the following formula in Equation 10.9, where n11 is the number of
empowered women in the treatment group, n10 is the number of empowered women
in the control group, n01 is the number of nonempowered women in the treatment
group, and n00 is the number of nonempowered women in the control group.
(10.9)√1
𝑛11+
1
𝑛10+
1
𝑛01+
1
𝑛00
Then we converted the log-odds ratios and their 95 per cent confidence intervals back
to odds ratios as well as to standardized mean differences using the formula to
transform log odds ratios to standardized mean differences. Following this
conversion, we converted the standardized mean difference (Cohen’s d) to Hedges’ g
to account for potential bias from small samples using the formula in Equation 10.10
to correct for potential bias from a small sample size:
(10.10) SMDcorrected = SMDuncorrected * (1 – 3
4∗(𝑛𝑡+𝑛𝑐−2)−1)
We were also able to estimate the variance and standard deviation of outcome
variables for which the standard deviation was not reported but for which the full
distribution was reported. For this purpose, we used the formula from Equation 10.11:
(10.11)𝑆𝐷 (𝑋) = √∑(𝑥−µ)2
𝑛−1
Here, µ is the mean value of x and n is the number of observations.
In the absence of standard errors for the regression analysis, we estimated the
standard error of the mean effect size by dividing the point estimate by the t-value
that is associated with significance at the 90, 95, and 99 per cent significance level,
respectively. This procedure ensured the estimation of conservative pooled standard
deviations.
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APPENDIX 11: ADDITIONAL FOREST PLOTS
Figure 11.1: Meta-Analysis for determining the effect of women’s self-help groups
on women’s economic empowerment based on quasi-experimental evaluations
with a high risk of selection-bias
Figure 11.2: Meta-Analysis for determining the effect of women’s self-help groups
on women’s economic empowerment based on quasi-experimental evaluations
with a medium risk of selection-bias
NOTE: Weights are from random effects analysis
Overall (I-squared = 42.1%, p = 0.178)
ID
Swendeman et al., 2009, India
Study
Osmani, 2007, Bangladesh
Nessa et al., 2012, Bangladesh
0.65 (0.33, 0.98)
ES (95% CI)
1.15 (0.47, 1.83)
0.37 (-0.10, 0.83)
0.65 (0.41, 0.89)
100.00
Weight
17.36
%
29.31
53.34
0.65 (0.33, 0.98)
ES (95% CI)
1.15 (0.47, 1.83)
0.37 (-0.10, 0.83)
0.65 (0.41, 0.89)
100.00
Weight
17.36
%
29.31
53.34
Impact SHGs on Economic Empowerment Based on High Risk of Bias Studies 0-1.83 0 1.83
NOTE: Weights are from random effects analysis
Overall (I-squared = 77.5%, p = 0.012)
Deininger and Liu, 2013 India
ID
De Hoop et al., 2014 India
Pitt et al., 2006, Bangladesh
Study
0.17 (0.03, 0.31)
0.28 (0.20, 0.36)
ES (95% CI)
0.03 (-0.21, 0.27)
0.12 (0.03, 0.21)
100.00
40.90
Weight
20.12
38.98
%
0.17 (0.03, 0.31)
0.28 (0.20, 0.36)
ES (95% CI)
0.03 (-0.21, 0.27)
0.12 (0.03, 0.21)
100.00
40.90
Weight
20.12
38.98
%
Impact SHGs on Economic Empowerment Based on Medium Risk of Bias Quasi-Experimental Studies 0-.358 0 .358
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Figure 11.3: Meta-Analysis for determining the effect of women’s self-help groups
on women’s economic empowerment based on RCTs and quasi-experimental
evaluations with a medium risk of selection-bias that focus on SHGs with a
training component
NOTE: Weights are from random effects analysis
Overall (I-squared = 16.7%, p = 0.308)
Deininger and Liu, 2013 India
De Hoop et al., 2014 India
ID
Kim et al., 2009 + Pronyk et al., 2006, South Africa
Sherman et al., 2010, India
Desai and Joshi, 2012, India
Study
0.26 (0.17, 0.35)
0.28 (0.20, 0.36)
0.03 (-0.21, 0.27)
ES (95% CI)
0.45 (0.06, 0.84)
0.30 (-0.11, 0.70)
0.28 (0.12, 0.45)
100.00
56.53
12.08
Weight
4.86
4.53
22.01
%
0.26 (0.17, 0.35)
0.28 (0.20, 0.36)
0.03 (-0.21, 0.27)
ES (95% CI)
0.45 (0.06, 0.84)
0.30 (-0.11, 0.70)
0.28 (0.12, 0.45)
100.00
56.53
12.08
Weight
4.86
4.53
22.01
%
Impact SHGs on Economic Empowerment RCTs and Medium Risk of Bias Quasi-Experimental Studies with Training
0-.842 0 .842
171 The Campbell Collaboration | www.campbellcollaboration.org
Figure 11.4: Meta-Analysis for determining the effect of women’s self-help groups
on women’s economic empowerment based on RCTs and quasi-experimental
evaluations with a medium risk of selection-bias that focus on SHGs without a
training component
Figure 11.5: Meta-Analysis for determining the effect of women’s self-help groups
on women’s social empowerment based on quasi-experimental evaluations with a
high risk of selection-bias
NOTE: Weights are from random effects analysis
Overall (I-squared = 77.9%, p = 0.034)
Banerjee et al., 2014 India
Pitt et al., 2006, Bangladesh
ID
Study
0.06 (-0.05, 0.16)
0.01 (-0.04, 0.05)
0.12 (0.03, 0.21)
ES (95% CI)
100.00
56.82
43.18
Weight
%
0.06 (-0.05, 0.16)
0.01 (-0.04, 0.05)
0.12 (0.03, 0.21)
ES (95% CI)
100.00
56.82
43.18
Weight
%
Impact SHGs on Economic Empowerment RCTs and Medium Risk of Bias Quasi-Experimental Studies without Training
0-.211 0 .211
NOTE: Weights are from random effects analysis
Overall (I-squared = 10.3%, p = 0.342)
Swendeman et al., 2009, India
Steel et al., 1998, Bangladesh
Nessa et al., 2012, Bangladesh
ID
Rosenberg et al., 2011, Haiti
Study
0.37 (0.18, 0.56)
0.88 (-0.89, 2.65)
0.32 (0.16, 0.49)
0.79 (0.26, 1.32)
ES (95% CI)
0.22 (-0.24, 0.69)
100.00
1.18
71.10
12.09
Weight
15.63
%
0.37 (0.18, 0.56)
0.88 (-0.89, 2.65)
0.32 (0.16, 0.49)
0.79 (0.26, 1.32)
ES (95% CI)
0.22 (-0.24, 0.69)
100.00
1.18
71.10
12.09
Weight
15.63
%
Impact SHGs on Social Empowerment Based on High Risk of Bias Studies 0-2.65 0 2.65
172 The Campbell Collaboration | www.campbellcollaboration.org
Figure 11.6: Meta-Analysis for determining the effect of women’s self-help groups
on women’s social empowerment based on quasi-experimental evaluations with a
medium risk of selection-bias
NOTE: Weights are from random effects analysis
Overall (I-squared = 0.0%, p = 0.682)
Study
Pitt et al., 2006, Bangladesh
ID
De Hoop et al., 2014 India
Deininger and Liu, 2013 India
0.13 (0.07, 0.19)
0.12 (0.03, 0.22)
ES (95% CI)
0.04 (-0.20, 0.27)
0.15 (0.07, 0.22)
100.00
%
39.35
Weight
5.95
54.71
0.13 (0.07, 0.19)
0.12 (0.03, 0.22)
ES (95% CI)
0.04 (-0.20, 0.27)
0.15 (0.07, 0.22)
100.00
%
39.35
Weight
5.95
54.71
Impact SHGs on Social Empowerment Based on Quasi-Experimental Medium Risk of Bias Studies 0-.275 0 .275
173 The Campbell Collaboration | www.campbellcollaboration.org
Figure 11.7: Meta-Analysis for determining the effect of women’s self-help groups
on women’s family-size decision making power based on quasi-experimental
evaluations with a high risk of selection-bias
Figure 11.8: Meta-Analysis for determining the effect of women’s self-help groups
on women’s family-size decision-making based on RCTs and quasi-experimental
evaluations with a medium risk of selection-bias that focus on SHGs with a
training component
NOTE: Weights are from random effects analysis
Overall (I-squared = 83.4%, p = 0.000)
Nessa et al., 2012, Bangladesh
Rosenberg et al., 2011, Haiti
Steel et al., 1998, Bangladesh
Study
ID
Mahmud, 1994, Bangladesh
0.53 (0.22, 0.85)
0.79 (0.63, 0.95)
0.22 (-0.24, 0.69)
0.32 (0.16, 0.49)
ES (95% CI)
0.74 (0.30, 1.18)
100.00
30.46
19.29
30.18
%
Weight
20.08
0.53 (0.22, 0.85)
0.79 (0.63, 0.95)
0.22 (-0.24, 0.69)
0.32 (0.16, 0.49)
ES (95% CI)
0.74 (0.30, 1.18)
100.00
30.46
19.29
30.18
%
Weight
20.08
Impact SHGs on Family-Size Decision Making Based on High Risk of Bias Studies 0-1.18 0 1.18
NOTE: Weights are from random effects analysis
Overall (I-squared = 41.4%, p = 0.181)
Kim et al., 2009 + Pronyk et al., 2006, South Africa
Desai and Joshi, 2012, India
Desai and Tarozzi, 2013, Ethiopia
ID
Study
0.41 (0.19, 0.63)
0.49 (0.25, 0.73)
0.45 (0.25, 0.66)
-0.23 (-0.96, 0.50)
ES (95% CI)
100.00
42.55
48.99
8.47
Weight
%
0.41 (0.19, 0.63)
0.49 (0.25, 0.73)
0.45 (0.25, 0.66)
-0.23 (-0.96, 0.50)
ES (95% CI)
100.00
42.55
48.99
8.47
Weight
%
Impact SHGs on Family-Size Decision Making Based on RCTs and Medium Risk of Bias Studies
0-.96 0 .96
174 The Campbell Collaboration | www.campbellcollaboration.org
Figure 11.9: Meta-Analysis for determining the effect of women’s self-help groups
on women’s mobility based on RCTs and quasi-experimental evaluations with a
medium risk of selection-bias that focus on SHGs with a training component
NOTE: Weights are from random effects analysis
Overall (I-squared = 0.0%, p = 0.392)
De Hoop et al., 2014 India
Study
Deininger and Liu, 2013 India
ID
0.14 (0.06, 0.21)
0.04 (-0.20, 0.27)
0.15 (0.07, 0.22)
ES (95% CI)
100.00
9.80
%
90.20
Weight
0.14 (0.06, 0.21)
0.04 (-0.20, 0.27)
0.15 (0.07, 0.22)
ES (95% CI)
100.00
9.80
%
90.20
Weight
Impact Self-Help Groups on Mobility Based on Low or Medium Risk of Bias Studies and training 0-.275 0 .275
175 The Campbell Collaboration | www.campbellcollaboration.org
APPENDIX 12: ADDITIONAL QUOTES BY THEME
Psychological Empowerment
Agentic Voice
Author Quotation
Kabeer, 2011, Bangladesh “If I have money, I can meet the needs of the stomach; I can buy a new sari and keep it in stock; I can go into society and speak out holding my head high; I can send my children to school. But if I have no land or money, I cannot speak.”
Kumari, 2011, South India “One of the things I have learned is to be able to speak in front of a group of five people without shivering.”
Kilby, 2011, South India “In one SHG, a member referred to having been ‘introverted’ from harassment, but as a result of the self-help group programme had become ‘bold’ and gained her ‘voice’.”
Dahal, 2014, Nepal “My confidence level is increasing. Before, I was afraid to speak out what I disliked, but now I am not dependent on anyone and I can speak my thoughts and I don’t care whether someone likes it or not”
Ramachandar & Pelto, 2009, South India “Now I understand how to talk to educated urban people.”
Participation in Household Negotiations
Author Quotation
Kabeer, 2011, Bangladesh “No man in this village ever made a land deed in their wives’ names, but now they are registering deposit savings schemes and insurance policies in their wives’ names.”
Dahal, 2014, Nepal
“I have realized that my views and comments are helpful in making a decision. If it is a family, decisions must be mutual.”
Kumari, 2011, South India “When children are not well, the wife takes the children to hospital even if the husband is not around.”
Mercer, 2002, Tanzania “Being allowed to have money and decide on how to spend it has brought us development in our household and now husbands give us the freedom to do our own things.”
Ramachandar & Pelto, 2009, South India “After two years, they [husband and in-laws] understood the value of the women’s groups and remained silent.”
176 The Campbell Collaboration | www.campbellcollaboration.org
Domestic Disputes
Author Quotation
Dahal, 2014, Nepal “The group members came to my house and dealt with my husband and mother in law. I did not want my husband to get jailed but wanted him to behave properly with me. The counseling of the group has helped me have a normal life back again”
Kabeer, 2011, Bangladesh “Nowadays husbands in villages don’t beat their wives so much. They realize that their wives also work.”
Kilby, 2011, South India “Seeing the women free from violence and ill-treatment at a community level and personal level [that] was the strongest form of accountability”
Kumari, 2011, South India “You cannot come drunk and batter me, my SHG will question you if you touch me, you should be prepared to answer them”
Ramachandar & Pelto, 2009, South India “My husband used to beat me for joining the [SHG] and my in-laws insisted that he beat me, but I stayed silent and today he does not dare to touch me.”
Kabeer, 2011, Bangladesh “If a husband is beating the daylights out of his wife, five of us women go there and warn him not to make trouble. Because we took this training for arbitration, we are able to talk like this. I could not have done this earlier.”
Mathrani & Pariodi, 2006, South India “If our husbands harass us, we do not feel intimidated as we now have a refuge to which we can take our recourse.”
Knowles, 2014, South India “Women in SHG find less fighting between husbands of SHG members due to influence and allegiance of SHG members ... more harmony in the village ... more unity between women and men because of the SHG”
Sahu & Singh, 2012, South India “Previously my husband used to shout if I had not cooked on time, but now, he adjusts if some day, I am late due to group meetings”.
Improved Networking
Author Quotation
Knowles, 2014, South India “SHG members complain if a tap is broken or if there is stagnant water ... they bring this to the panchayat [village leader] president’s attention issues in the community ... if they have other difficulties they go to government officials now”
Kabeer, 2011, Bangladesh “Earlier if I saw a group of people sitting together, I did not have the courage to go up to them and say anything. Now even if there are 100 people sitting together, I can go up to them and have my say. Earlier, if we saw a policeman on the road, we would run away. Now even if we go to court, we can talk to policemen there.”
Kilby, 2011, South India “The women themselves insisted on dealing with the tractor owners directly and ‘held out’ for three weeks before the tractor owners agreed to deal with the women directly. It was the close interaction with staff at
177 The Campbell Collaboration | www.campbellcollaboration.org
all levels, which gave the women the confidence to deal with higher caste village people in this way.”
Kumari, 2011, South India “I went to the panchayat [village leadership]. They asked me where I was from. I said I belong to [the self-help group]. Immediately the staff was asked to take the record and hand it over to me. A [record] was given to me immediately. It was then that I understood the value of belonging to [the SHG].”
Solidarity
Author Quotation
Dahal, 2014, Nepal “Our strength is that we have some common problems which we have to solve together. We were deprived of our rights and respect for years and this agony has helped us to move together and form a unity.”
Kabeer, 2011, Bangladesh “One stick can be broken, a bundle of sticks cannot. It is not possible to achieve anything on one’s own. You have no value on your own. Now if I am ill, my [SHG] members will look after me.”
Kumari, 2011, South India “Women now have the courage to address [unfair] matters because they say, ‘I am not alone. The group members are behind me.’”
Mathrani & Pariodi, 2006, South India “If we disapprove of something, we are able to express our opinions to the larger community as we have a collective voice.”
Community Respect
Author Quotation
Dahal, 2014, Nepal “The society’s view upon being a SHG member has changed. Before it was against the social norms to go out of a house but now society praises women who are involved in SHGs”
Kabeer, 2011, Bangladesh “There is no proper treatment or medicine in hospitals. We have demonstrated in [our] town, demanding our rights and protesting against the corruption of doctors and theft of public medicine. So now when they hear at the hospital that someone is from our [SHG], they give them a bit more respect.”
Kumari, 2011, South India “When people know we are from GSGSK, we are given special consideration. They give us a chair to sit wherever we go.”
Ramachandar & Pelto, 2009, South India “The biggest benefit of the [SHG] is that we get prestige and honour in our community; we gain experience going to the bank and meeting with officials.”
Financial Skills
Author Quotation
Kumari, 2011, South India “The fear of handling money is gone.”
178 The Campbell Collaboration | www.campbellcollaboration.org
Mercer, 2002, Tanzania “Being allowed to have money and decide on how to spend it has brought us development in our household and now husbands give us the freedom to do our own things.”
Knowles, 2014, South India “Women can go to the bank now without husbands.”
Ramachandar & Pelto, 2009, South India “I handled all the money matters, including buying and selling of chickens and meat.”
Maclean, 2012, Bolivia “The interest rate is really high. Don Pedro—my husband—tells me off: ‘Why are you just working for that [the credit]. You’re just working for the bank, and the interest is really expensive!’”
Mathrani & Pariodi, 2006, South India “What kind of structure have these women constructed? They are like monkeys, if we hit their home it will collapse.”
Catalyzing Broader Social Action
Author Quotation
Knowles, 2014, South India “SHG members [have] become councillors, government officials ... those elected [in] six out of 15 wards are women and members of elected panchayat bodies. They advanced their skills and were respected by the community.
Sahu & Singh, 2012, South India “In the previous election, the MLA candidate had promised to build a toad but he did not. When he came for campaigning this time, we questioned him for not keeping his promise and we didn't vote him either.”
Understanding Political Context
Author Quotation
Pattenden, 2011, South India “A group from another village who had approached the GP [Gram Panchayat—local government] building to request the disbursement of anti-poverty resources had been stoned.”
Kumari, 2011, South India “Empowerment? There has not been complete empowerment. More factors are needed like equal wages. I would say that only 5 to 10% of empowerment has happened.”
Adverse Outcomes
Barriers to Participation
Author Quotation
Dahal, 2014, South India “The issue of selection bias can be agreed to a certain extent acknowledging to the fact that very poor people cannot afford the membership fee and enough time for group activities.”
179 The Campbell Collaboration | www.campbellcollaboration.org
Mercer, 2002, Tanzania “Some women don't join because they feel inferior, they think that members are rich, can afford things and can be close to the Church, they are in good positions.”
Mathrani & Pariodi, 2006, South India “The larger community was of the view that sangha formation is relevant only for the lower castes and that women from the upper castes were demeaning themselves by getting involved in this work.”
Disappointment
Author Quotation
Maclean, 2012, Bolivia “SHGs operate at very low cost, have a small fund, raise little interest so we cannot accomplish bigger projects and this is our weakness.”
Mercer, 2002 Tanzania "Other women are discouraged because it is almost four to five years since we contributed the money for the cows and up to now we haven't seen any good profit."
Mistrust and Corruption
Author Quotation
Maclean, 2012, Bolivia “I don’t like [to be treasurer]. It’s dangerous. The money can disappear, you can get confused. Even Dona Feliza [a younger woman who was educated in la Paz] can get a little confused sometimes. And they talk about the treasurer and accuse her of things.”
Dahal, 2014, South India “Accounts are not maintained. The leaders of SHGs are heard to have lent the saved amounts to others at high interest rates for personal benefit.”
Stigma
Author Quotation
Ramachandar & Pelto, 2009, South India The men used to make comments such as, these women are doing “tamasha” (showing off) and they are going to close down our sangha after a few days. But we did not worry about those comments.”
Mathrani & Pariodi, 2006, South India “They think women are attending meetings to get money and take control of the village council.” “Men say that women are being overly ambitious.” "Upper castes say, 'These women attend meetings and visit the panchayat to get money. They are trying to usurp the position of the gowda and take control of the village.’"
180 The Campbell Collaboration | www.campbellcollaboration.org
10 Contribution of authors
The study was led by Carinne Brody (CB). This report was written by CB and
Thomas de Hoop (TH). CB, Megan Dunbar (MD), Padmini Murthy (PM) and Shari
Dworkin (SD) authored by study protocol. The search strategy was developed by CB,
MD and Tara Horvath, a search specialist from the University of California, San
Francisco. CB and RW, along with several research assistants, conducted the search.
The meta-analysis was undertaken by TH and Martina Vojtkova. The qualitative
synthesis was undertaken by CB and RW. RW edited the report. CB and TH will be
responsible for updating this review as additional evidence accumulates and as
funding becomes available.
181 The Campbell Collaboration | www.campbellcollaboration.org
11 Declarations of interest
The authors declare that we are not aware of any conflicts of interests. Thomas de
Hoop was involved in a primary study on the effects of women self-help groups on
women’s empowerment in Odisha, India. In the risk of bias assessment for this
study, we emphasized the opinion of the other reviewer of the quantitative risk of
bias assessment Martina Vojtkova rather than relying on the opinion of Thomas de
Hoop.
182 The Campbell Collaboration | www.campbellcollaboration.org
12 Sources of support
EXTERNAL SOURCES
Funder: International Initiative for Impact Evaluation